Nutils is a Free and Open Source Python programming library for Finite Element Method computations, developed by Evalf and distributed under the permissive MIT license. Key features are a readable, math centric syntax, an object oriented design, strict separation of topology and geometry, and high level function manipulations with support for automatic differentiation.

Nutils provides the tools required to construct a typical simulation workflow in just a few lines of Python code, while at the same time leaving full flexibility to build novel workflows or interact with third party tools. With native support for Isogeometric Analysis (IGA), the Finite Cell method (FCM), multi-physics, mixed methods, and hierarchical refinement, Nutils forms an excellent platform for numerical science. Efficient under-the-hood vectorization and built-in parallellisation provide for an effortless transition from academic research projects to full scale, real world applications.

# How to Read This Book

Since Nutils is a library for the development of numerical simulations, this book assumes that the reader is familiar with differential calculus, Galerkin methods, and the Finite Element Method. If this is not the case, chances are that Nutils is not the tool they are looking for.

First time users who are eager to get their feet wet will want to begin with the getting started guide and build a functioning Poisson solver in three easy steps, no questions asked. Following this, beginners are strongly advised to follow the hands on tutorial to gain an an in-depth understanding of Nutils concepts and get familiar with the syntax.

Novices and advanced users alike may find interest in the installation guide, which ranges from basic installation instructions to tips and tricks for optimizing the installation, instructions for running a Docker style container, and suggestions for computing remotely.

The release history provides an overview of changes between releases. This is the place to monitor for long term users who want to keep up to date with the latest and greatest new features. The release pages also provide links to the relevant API reference where all Nutils functions are documented.

Anybody looking to build their own Nutils simulations are encouraged to browse through the example projects. Most simulations will have components in common with existing scripts, so a mix-and-match approach is a good way to start building your own. In case questions do remain, the support page lists ways of getting in touch with developers.

Finally, the science section provides an overview of publications that use Nutils in their research. Reproducing results from these articles is a great starting point for follow-up research, as well as good scientific practice in its own right. Help others do the same by citing Nutils in your own publications!

# Getting Started

The following is a quick start guide to running your first Nutils simulation in three simple steps. Afterward, be sure to read the installation guide for extra installation instructions, study the tutorial to familiarize yourself with Nutils' concepts and syntax, and explore the examples for inspiration.

## Step 1: Install Nutils and Matplotlib

With Python version 3.7 or newer installed, Nutils and Matplotlib can be installed via the Python Package Index using the pip package installer. In a terminal window:

python -m pip install --user nutils matplotlib


Note that Nutils depends on Numpy, Treelog and Stringly, which means that these modules are pulled in automatically if they were not installed prior. Though most Nutils applications will require Matplotlib for visualization, it is not a dependency for Nutils itself, and is therefore installed explicitly.

## Step 2: Create a simulation script

Open a text editor and create a file poisson.py with the following contents:

from nutils import mesh, function, solver, export, cli

def main(nelems: int = 10, etype: str = 'square'):
domain, x = mesh.unitsquare(nelems, etype)
u = function.dotarg('udofs', domain.basis('std', degree=1))
J = function.J(x)
cons = solver.optimize('udofs',
domain.boundary.integral(u**2 * J, degree=2), droptol=1e-12)
udofs = solver.optimize('udofs',
domain.integral((g @ g / 2 - u) * J, degree=1), constrain=cons)
bezier = domain.sample('bezier', 3)
x, u = bezier.eval([x, u], udofs=udofs)
export.triplot('u.png', x, u, tri=bezier.tri, hull=bezier.hull)

cli.run(main)


Note that while we could make the script even shorter by avoiding the main function and cli.run, the above structure is preferred as it automatically sets up a logging environment, activates a matrix backend and handles command line parsing.

## Step 3: Run the simulation

Back in the terminal, the simulation can now be started by running:

python poisson.py


This should produce the following output:

nutils v7.0
optimize > constrained 40/121 dofs
optimize > optimum value 0.00e+00
optimize > solve > solving 81 dof system to machine precision using arnoldi solver
optimize > solve > solver returned with residual 6e-17
optimize > optimum value -1.75e-02
u.png


If the terminal is reasonably modern (Windows users may want to install the new Windows Terminal) then the messages are coloured for extra clarity. The last line of the log shows the location of the simultaneously generated html file that holds the same log, as well as a link to the generated image.

To run the same simulation on a mesh that is finer and made up or triangles instead of squares, arguments can be provided on the command line:

python poisson.py nelems=20 etype=triangle


# Installation

Nutils requires a working installation of Python 3.7 or higher. Many different installers exist and there are no known issues with any of them. When in doubt about which to use, a safe option is to go with the official installer. From there on Nutils can be installed following the steps below.

Depending on your system the Python executable may be installed as either python or python3, or both, not to mention alternative implementations such as pypy or pyston. In the following instructions, python is to be replaced with the relevant executable name.

# Installing Nutils

Nutils is installed via Python's Pip package installer, which most Python distributions install by default. In the following instructions we add the flag --user for a local installation that does not require system privileges, which is recommended but not required.

The following command installs the stable version of Nutils from the package archive, along with its dependencies Numpy, Treelog and Stringly:

python -m pip install --user nutils


To install the most recent development version we use Github's ability to generate zip balls:

python -m pip install --user --force-reinstall \


Alternatively, if the Git version control system is installed, we can use pip's ability to interact with it directly to install the same version as follows:

python -m pip install --user --force-reinstall \
git+https://github.com/evalf/nutils.git@master


This notation has the advantage that even a specific commit (rather than a branch) can be installed directly by specifying it after the @.

Finally, if we do desire a checkout of Nutils' source code, for instance to make changes to it, then we can instruct pip to install directly from the location on disk:

git clone https://github.com/evalf/nutils.git
cd nutils
python -m pip install --user .


In this scenario it is possible to add the --editable flag to install Nutils by reference, rather than by making a copy, which is useful in situations of active development. Note, however, that pip requires manual intervention to revert back to a subsequent installation by copy.

# Installing a matrix backend

Nutils currently supports three matrix backends: Numpy, Scipy and MKL. Since Numpy is a primary dependency this backend is always available. Unfortunately it is also the least performant of the three because of its inability to exploit sparsity. It is therefore strongly recommended to install one of the other two backends via the instructions below.

By default, Nutils automatically activates the best available matrix backend: MKL, Scipy or Numpy, in that order. A consequence of this is that a faulty installation may easily go unnoticed as Nutils will silently fall back on a lesser backend. As such, to make sure that the installation was successful it is recommended to force the backend at least once by setting the NUTILS_MATRIX environment variable. In Linux:

NUTILS_MATRIX=MKL python myscript.py


## Scipy

The Scipy matrix backend becomes available when Scipy is installed, either using the platform's package manager or via pip:

python -m pip install --user scipy


In addition to a sparse direct solver, the Scipy backend provides many iterative solvers such as CG, CGS and GMRES, as well as preconditioners. The direct solver can optionally be made more performant by additionally installing the scikit-umfpack module.

## MKL

Intel's oneAPI Math Kernel Library provides the Pardiso sparse direct solver, which is easily the most powerful direct solver that is currently supported. It is installed via the official instructions, or, if applicable, by any of the steps below.

On a Debian based Linux system (such as Ubuntu) the libraries can be directly installed via the package manager:

sudo apt install libmkl-rt


For Fedora or Centos Linux, Intel maintains its own repository that can be added with the following steps:

sudo dnf config-manager --add-repo https://yum.repos.intel.com/mkl/setup/intel-mkl.repo
sudo rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
sudo dnf install intel-mkl
sudo tee /etc/ld.so.conf.d/mkl.conf << EOF > /dev/null
/opt/intel/lib/intel64/
/opt/intel/mkl/lib/intel64/
EOF
sudo ldconfig -v


# Quality of Life

Here we list some modules that are not direct requirements, but that can be used in conjunction with Nutils to make life a little bit better.

## BottomBar

BottomBar is a context manager for Python that prints a status line at the bottom of a terminal window. When it is installed, cli.run automatically activates it to display the location of the html log (rather than only logging it at the beginning and end of the simulation) as well as runtime and memory usage information.

python -m pip install bottombar


# Improving performance

While Nutils is not (yet) the fastest tool in its class, with some effort it is possible to achieve sufficient performance to allow simulations of over a million degrees of freedom. The matrix backend is the most important thing to get right, but there are a few other factors that are worth considering.

## Enable parallel processing

On multi-core architectures, the most straightforward acceleration path available is to use parallel assembly, activated using the NUTILS_NPROCS environment variable. Both Linux and OS X both are supported. Unfortunately, the feature is currently disabled on Windows as it does not support the fork system call that is used by the current implementation.

On Windows, the easiest way to enjoy parallel speedup is to make use of the new Windows Subsystem for Linux (WSL2), which is complete Linux environment running on top of Windows. To install it simply select one of the many Linux distributions from the Windows store, such as Ubuntu 20.04 LTS or Debian GNU/Linux.

Many Numpy installations default to using the openBLAS library to provide its linear algebra routines, which supports multi-threading using the openMP parallelization standard. While this is useful in general, it is in fact detrimental in case Nutils is using parallel assembly, in which case the numerical operations are best performed sequentially. This can be achieved by setting the OMP_NUM_THREADS environment variable.

In Linux this can be done permanently by adding the following line to the shell's configuration file. In Linux this is typically ~/.bashrc:

export OMP_NUM_THREADS=1


The downside to this approach is that multithreading is disabled for all applications that use openBLAS, not just Nutils. Alternatively in Linux the setting can be specified one-off in the form of a prefix:

OMP_NUM_THREADS=1 NUTILS_NPROCS=8 python myscript.py


## Consider a faster interpreter

The most commonly used Python interpreter is without doubt the CPython reference implementation, but it is not the only option. Before taking an application in production it may be worth testing if other implementations have useful performance benefits.

One interpreter of note is Pyston, which brings just-in-time compilation enhancements that in a typical application can yield a 20% speed improvement. After Pyston is installed, Nutils and dependencies can be installed as before simply replacing python by pyston3. As packages will be installed from source some development libraries may need to be installed, but what is missing can usually be inferred from the error messages.

# Using Containers

As an alternative to installing Nutils, it is possible to download a preinstalled system image with all the above considerations taken care of. Nutils provides OCI compatible containers for all releases, as well as the current developement version, which can be run using tools such as Docker or Podman. The images are hosted in Github's container repository.

The container images include all the official examples. To run one, add the name of the example and any additional arguments to the command line. For example, you can run example laplace using the latest version of Nutils with:

docker run --rm -it ghcr.io/evalf/nutils:latest laplace


HTML log files are generated in the /log directory of the container. If you want to store the log files in /path/to/log on the host, add -v /path/to/log:/log to the command line before the name of the image. Extending the previous example:

docker run --rm -it -v /path/to/log:/log ghcr.io/evalf/nutils:latest laplace


To run a Python script in this container, bind mount the directory containing the script, including all files necessary to run the script, to /app in the container and add the relative path to the script and any arguments to the command line. For example, you can run /path/to/myscript.py with Docker using:

docker run --rm -it -v /path/to:/app:ro ghcr.io/evalf/nutils:latest myscript.py


# Remote Computing

Computations beyond a certain size are usually moved to a remote computing facility, typically accessed using tools such as Secure Shell or Mosh, combined with a terminal multiplexer such as GNU Screen or Tmux. In this scenario it is useful to install a webserver for remote viewing of the html logs.

The standard ~/public_html output directory is configured with the scenario in mind, as the Apache webserver uses this as the default user directory. As this is disabled by default, the module needs to be enabled by editing the relevant configuration file or, in Debian Linux, by using the a2enmod utility:

sudo a2enmod userdir


Similar behaviour can be achieved with the Nginx by configuring a location pattern in the appropriate server block:

location ~ ^/~(.+?)(/.*)?${ alias /home/$1/public_html\$2;
}


Finally, the terminal output can be made to show the http address rather than the local uri by adding the following line to the ~/.nutilsrc configuration file:

outrooturi = 'https://mydomain.tld/~myusername/'


# Tutorial

In this tutorial we will explore Nutils' main building blocks by solving a simple 1D Laplace problem. The tutorial assumes knowledge of the Python programming language, as well as familiarity with the third party modules Numpy and Matplotlib. It also assumes knowledge of advanced calculus, weak formulations, and the Finite Element Method, and makes heavy use of Einstein notation.

The computation that we will work towards amounts to about 20 lines of Nutils code, including visualization. The entire script is presented below, in copy-pasteable form suitable for interactive exploration using for example ipython. In the sections that follow we will go over these lines ones by one and explain the relevant concepts involved.

from nutils import function, mesh, solver
from nutils.expression_v2 import Namespace
import numpy
from matplotlib import pyplot as plt

topo, geom = mesh.rectilinear([numpy.linspace(0, 1, 5)])

ns = Namespace()
ns.x = geom
ns.basis = topo.basis('spline', degree=1)
ns.u = function.dotarg('lhs', ns.basis)

sqr = topo.boundary['left'].integral('u^2 dS' @ ns, degree=2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# optimize > constrained 1/5 dofs
# optimize > optimum value 0.00e+00

res = topo.integral('∇_i(basis_n) ∇_i(u) dV' @ ns, degree=0)
res -= topo.boundary['right'].integral('basis_n dS' @ ns, degree=0)
lhs = solver.solve_linear('lhs', residual=res, constrain=cons)
# solve > solving 4 dof system to machine precision using arnoldi solver
# solve > solver returned with residual 9e-16±1e-15

bezier = topo.sample('bezier', 32)
nanjoin = lambda array, tri: numpy.insert(array.take(tri.flat, 0).astype(float),
slice(tri.shape[1], tri.size, tri.shape[1]), numpy.nan, axis=0)
sampled_x = nanjoin(bezier.eval('x_0' @ ns), bezier.tri)
def plot_line(func, **arguments):
plt.plot(sampled_x, nanjoin(bezier.eval(func, **arguments), bezier.tri))
plt.xlabel('x_0')
plt.xticks(numpy.linspace(0, 1, 5))

plot_line(ns.u, lhs=lhs)


You are encouraged to execute this code at least once before reading on, as the code snippets that follow may assume certain products to be present in the namespace. In particular the plot_line function is used heavily in the ensuing sections.

# A Little Bit of Theory

We will introduce fundamental Nutils concepts based on the 1D homogeneous Laplace problem,

$u''(x) = 0$

with boundary conditions $$u(0) = 0$$ and $$u'(1) = 1$$. Even though the solution is trivially found to be $$u(x) = x$$, the example serves to introduce many key concepts in the Nutils paradigm, concepts that can then be applied to solve a wide class of physics problems.

## Weak Form

A key step to solving a problem using the Finite Element Method is to cast it into weak form.

Let $$Ω$$ be the unit line $$[0,1]$$ with boundaries $$Γ_\text{left}$$ and $$Γ_\text{right}$$, and let $$H_0(Ω)$$ be a suitable function space such that any $$u ∈ H_0(Ω)$$ satisfies $$u = 0$$ in $$Γ_\text{left}$$. The Laplace problem is solved uniquely by the element $$u ∈ H_0(Ω)$$ for which $$R(v, u) = 0$$ for all test functions $$v ∈ H_0(Ω)$$, with $$R$$ the bilinear functional

$R(v, u) := ∫_Ω \frac{∂v}{∂x_i} \frac{∂u}{∂x_i} \ dV - ∫_{Γ_\text{right}} v \ dS.$

## Discrete Solution

The final step before turning to code is to make the problem discrete.

To restrict ourselves to a finite dimensional subspace we adopt a set of Finite Element basis functions $$φ_n ∈ H_0(Ω)$$. In this space, the Finite Element solution is established by solving the linear system of equations $$R_n(\hat{u}) = 0$$, with residual vector $$R_n(\hat{u}) := R(φ_n, \hat{u})$$, and discrete solution

$\hat{u}(x) = φ_n(x) \hat{u}_n.$

Note that discretization inevitably implies approximation, i.e. $$u ≠ \hat{u}$$ in general. In this case, however, we choose $${φ_n}$$ to be the space of piecewise linears, which contains the exact solution. We therefore expect our Finite Element solution to be exact.

# Topology vs Geometry

Rather than having a single concept of what is typically referred to as the 'mesh', Nutils maintains a strict separation of topology and geometry. The nutils.topology.Topology represents a collection of elements and inter-element connectivity, along with recipes for creating bases. It has no (public) notion of position. The geometry takes the nutils.topology.Topology and positions it in space. This separation makes it possible to define multiple geometries belonging to a single nutils.topology.Topology, a feature that is useful for example in certain Lagrangian formulations.

While not having mesh objects, Nutils does have a nutils.mesh module, which hosts functions that return tuples of topology and geometry. Nutils provides two builtin mesh generators: nutils.mesh.rectilinear, a generator for structured topologies (i.e. tensor products of one or more one-dimensional topologies), and nutils.mesh.unitsquare, a unit square mesh generator with square or triangular elements or a mixture of both. The latter is mostly useful for testing. In addition to generators, Nutils also provides the nutils.mesh.gmsh importer for gmsh-generated meshes.

The structured mesh generator takes as its first argument a list of element vertices per dimension. A one-dimensional topology with four elements of equal size between 0 and 1 is generated by

mesh.rectilinear([[0, 0.25, 0.5, 0.75, 1.0]])
# (StructuredTopology<4>, Array<1>)


Alternatively we could have used numpy.linspace to generate a sequence of equidistant vertices, and unpack the resulting tuple:

topo, geom = mesh.rectilinear([numpy.linspace(0, 1, 5)])


We will use this topology and geometry throughout the remainder of this tutorial.

Note that the argument is a list of length one: this outer sequence lists the dimensions, the inner the vertices per dimension. To generate a two-dimensional topology, simply add a second list of vertices to the outer list. For example, an equidistant topology with four by eight elements with a unit square geometry is generated by

mesh.rectilinear([numpy.linspace(0, 1, 5), numpy.linspace(0, 1, 9)])
# (StructuredTopology<4x8>, Array<2>)


Any topology defines a boundary via the nutils.topology.Topology.boundary attribute. Optionally, a topology can offer subtopologies via the getitem operator. The rectilinear mesh generator automatically defines 'left' and 'right' boundary groups for the first dimension, making the left boundary accessible as:

topo.boundary['left']
# StructuredTopology<>


Optionally, a topology can be made periodic in one or more dimensions by passing a list of dimension indices to be periodic via the keyword argument periodic. For example, to make the second dimension of the above two-dimensional mesh periodic, add periodic=[1]:

mesh.rectilinear([numpy.linspace(0, 1, 5), numpy.linspace(0, 1, 9)], periodic=[1])
# (StructuredTopology<4x8p>, Array<2>)


Note that in this case the boundary topology, though still available, is empty.

# Bases

In Nutils, a basis is a vector-valued function object that evaluates, in any given point $$ξ$$ on the topology, to the full array of basis function values $$φ_0(ξ), φ_1(ξ), \dots, φ_{n-1}(ξ)$$. It must be pointed out that Nutils will in practice operate only on the basis functions that are locally non-zero, a key optimization in Finite Element computations. But as a concept, it helps to think of a basis as evaluating always to the full array.

Several nutils.topology.Topology objects support creating bases via the Topology.basis() method. A nutils.topology.StructuredTopology, as generated by nutils.mesh.rectilinear, can create a spline basis with arbitrary degree and arbitrary continuity. The following generates a degree one spline basis on our previously created unit line topology topo:

basis = topo.basis('spline', degree=1)


The five basis functions are

plot_line(basis)


We will use this basis throughout the following sections.

Change the degree argument to 2 for a quadratic spline basis:

plot_line(topo.basis('spline', degree=2))


By default the continuity of the spline functions at element edges is the degree minus one. To change this, pass the desired continuity via keyword argument continuity. For example, a quadratic spline basis with $$C^0$$ continuity is generated with

plot_line(topo.basis('spline', degree=2, continuity=0))


$$C^0$$ continuous spline bases can also be generated by the 'std' basis:

plot_line(topo.basis('std', degree=2))


The 'std' basis is supported by topologies with square and/or triangular elements without hanging nodes.

Discontinuous basis functions are generated using the 'discont' type, e.g.

plot_line(topo.basis('discont', degree=2))


# Functions

A function in Nutils is a mapping from a topology onto an n-dimensional array, and comes in the form of a functions: nutils.function.Array object. It is not to be confused with Python's own function objects, which operate on the space of general Python objects. Two examples of Nutils functions have already made the scene: the geometry geom, as returned by nutils.mesh.rectilinear, and the bases generated by Topology.basis(). Though seemingly different, these two constructs are members of the same class and in fact fully interoperable.

The nutils.function.Array functions behave very much like numpy.ndarray objects: the functions have a nutils.function.Array.shape, nutils.function.Array.ndim and a nutils.function.Array.dtype:

geom.shape
# (1,)
basis.shape
# (5,)
geom.ndim
# 1
geom.dtype
# <class 'float'>


The functions support numpy-style indexing. For example, to get the first element of the geometry geom you can write geom[0] and to select the first two basis functions you can write

plot_line(basis[:2])


The usual unary and binary operators are available:

plot_line(geom[0]*(1-geom[0])/2)


Several trigonometric functions are defined in the nutils.function module. An example with a sine function:

plot_line(function.sin(2*geom[0]*numpy.pi))


The dot product is available via nutils.function.dot. To contract the basis with an arbitrary coefficient vector:

plot_line(function.dot(basis, [1,2,0,5,4]))


Recalling the definition of the discrete solution, the above is precisely the way to evaluate the resulting function. What remains now is to establish the coefficients for which this function solves the Laplace problem.

## Arguments

A discrete model is often written in terms of an unknown, or a vector of unknowns. In Nutils this translates to a function argument, nutils.function.Argument. Usually an argument is used in an inner product with a basis. For this purpose there exists the nutils.function.dotarg function. For example, the discrete solution can be written as

ns.u = function.dotarg('lhs', ns.basis)


with the argument identified by 'lhs' the vector of unknowns $$\hat{u}_n )). # Namespace Nutils functions behave entirely like Numpy arrays, and can be manipulated as such, using a combination of operators, object methods, and methods found in the nutils.function module. Though powerful, the resulting code is often lengthy, littered with colons and brackets, and hard to read. Namespaces provide an alternative, cleaner syntax for a prominent subset of array manipulations. A nutils.expression_v2.Namespace is a collection of nutils.function.Array functions. An empty nutils.expression_v2.Namespace is created as follows: ns = Namespace()  New entries are added to a nutils.expression_v2.Namespace by assigning an nutils.function.Array to an attribute. For example, to assign the geometry geom to ns.x, simply type ns.x = geom  You can now use ns.x where you would use geom. Usually you want to add the gradient, normal and jacobian of this geometry to the namespace as well. This can be done using nutils.expression_v2.Namespace.define_for naming the geometry (as present in the namespace) and names for the gradient, normal, and the jacobian as keyword arguments: ns.define_for('x', gradient='∇', normal='n', jacobians=('dV', 'dS'))  Note that any keyword argument is optional. To assign a linear basis to ns.basis, type ns.basis = topo.basis('spline', degree=1)  and to assign the discrete solution as the inner product of this basis with argument 'lhs', type ns.u = function.dotarg('lhs', ns.basis)  You can also assign numbers and numpy.ndarray objects: ns.a = 1 ns.b = 2 ns.c = numpy.array([1,2]) ns.A = numpy.array([[1,2],[3,4]])  ## Expressions In addition to inserting ready objects, a namespace's real power lies in its ability to be assigned string expressions. These expressions may reference any nutils.function.Array function present in the nutils.expression_v2.Namespace, and must explicitly name all array dimensions, with the object of both aiding readibility and facilitating high order tensor manipulations. A short explanation of the syntax follows; see nutils.expression_v2 for the complete documentation. A term is written by joining variables with spaces, optionally preceeded by a single number, e.g. 2 a b. A fraction is written as two terms joined by /, e.g. 2 a / 3 b, which is equivalent to (2 a) / (3 b). An addition or subtraction is written as two terms joined by + or -, respectively, e.g. 1 + a b - 2 b. Exponentation is written by two variables or numbers joined by ^, e.g. a^2. Several trigonometric functions are available, e.g. 0.5 sin(a). Assigning an expression to the namespace is then done as follows. ns.e = '2 a / 3 b' ns.e = (2*ns.a) / (3*ns.b) # equivalent w/o expression  The resulting ns.e is an ordinary nutils.function.Array. Note that the variables used in the expression should exist in the namespace, not just as a local variable: localvar = 1 ns.f = '2 localvar' # Traceback (most recent call last): # ... # nutils.expression_v2.ExpressionSyntaxError: No such variable: localvar. # 2 localvar # ^^^^^^^^  When using arrays in an expression all axes of the arrays should be labelled with an index, e.g. 2 c_i and c_i A_jk. Repeated indices are summed, e.g. A_ii is the trace of d and A_ij c_j is the matrix-vector product of d and c. You can also insert a number, e.g. c_0 is the first element of c. All terms in an expression should have the same set of indices after summation, e.g. it is an error to write c_i + 1. When assigning an expression with remaining indices to the namespace, the indices should be listed explicitly at the left hand side: ns.f_i = '2 c_i' ns.f = 2*ns.c # equivalent w/o expression  The order of the indices matter: the resulting nutils.function.Array will have its axes ordered by the listed indices. The following three statements are equivalent: ns.g_ijk = 'c_i A_jk' ns.g_kji = 'c_k A_ji' ns.g = ns.c[:,numpy.newaxis,numpy.newaxis]*ns.A[numpy.newaxis,:,:] # equivalent w/o expression  Function ∇, introduced to the namespace with ~nutils.expression_v2.Namespace.define_for using geometry ns.x, returns the gradient of a variable with respect ns.x, e.g. the gradient of the basis is ∇_i(basis_n). This works with expressions as well, e.g. ∇_i(2 basis_n + basis_n^2) is the gradient of 2 basis_n + basis_n^2. ## Manual evaluation Sometimes it is useful to evaluate an expression to an nutils.function.Array without inserting the result in the namespace. This can be done using the <expression> @ <namespace> notation. An example with a scalar expression: '2 a / 3 b' @ ns # Array<> (2*ns.a) / (3*ns.b) # equivalent w/o ... @ ns # Array<>  An example with a vector expression: '2 c_i' @ ns # Array<2> 2*ns.c # equivalent w/o ... @ ns # Array<2>  If an expression has more than one remaining index, the axes of the evaluated array are ordered alphabetically: 'c_i A_jk' @ ns # Array<2,2,2> ns.c[:,numpy.newaxis,numpy.newaxis]*ns.A[numpy.newaxis,:,:] # equivalent w/o ... @ ns # Array<2,2,2>  # Integrals A central operation in any Finite Element application is to integrate a function over a physical domain. In Nutils, integration starts with the topology, in particular the integral() method. The integral method takes a nutils.function.Array function as first argument and the degree as keyword argument. The function should contain the Jacobian of the geometry against which the function should be integrated, using either nutils.function.J or dV in a namespace expression (assuming the jacobian has been added to the namespace using ns.define_for(..., jacobians=('dV', 'dS'))). For example, the following integrates 1 against geometry x: I = topo.integral('1 dV' @ ns, degree=0) I # Array<>  The resulting nutils.function.Array object is a representation of the integral, as yet unevaluated. To compute the actual numbers, call the Array.eval() method: I.eval() # 1.0±1e-15  Be careful with including the Jacobian in your integrands. The following two integrals are different: topo.integral('(1 + 1) dV' @ ns, degree=0).eval() # 2.0±1e-15 topo.integral('1 + 1 dV' @ ns, degree=0).eval() # 5.0±1e-15  Like any other nutils.function.Array, the integrals can be added or subtracted: J = topo.integral('x_0 dV' @ ns, degree=1) (I+J).eval() # 1.5±1e-15  Recall that a topology boundary is also a nutils.topology.Topology object, and hence it supports integration. For example, to integrate the geometry x over the entire boundary, write topo.boundary.integral('x_0 dS' @ ns, degree=1).eval() # 1.0±1e-15  To limit the integral to the right boundary, write topo.boundary['right'].integral('x_0 dS' @ ns, degree=1).eval() # 1.0±1e-15  Note that this boundary is simply a point and the integral a point evaluation. Integrating and evaluating a 1D nutils.function.Array results in a 1D numpy.ndarray: >>> topo.integral('basis_i dV' @ ns, degree=1).eval() array([0.125, 0.25 , 0.25 , 0.25 , 0.125])±1e-15  Since the integrals of 2D nutils.function.Array functions are usually sparse, the Array.eval() <nutils.function.Array.eval> method does not return a dense numpy.ndarray, but a Nutils sparse matrix object: a subclass of nutils.matrix.Matrix. Nutils interfaces several linear solvers (more on this in Section solvers below) but if you want to use a custom solver you can export the matrix to a dense, compressed sparse row or coordinate representation via the Matrix.export() method. An example: M = topo.integral('∇_i(basis_m) ∇_i(basis_n) dV' @ ns, degree=1).eval() M.export('dense') # array([[ 4., -4., 0., 0., 0.], # [-4., 8., -4., 0., 0.], # [ 0., -4., 8., -4., 0.], # [ 0., 0., -4., 8., -4.], # [ 0., 0., 0., -4., 4.]])±1e-15 M.export('csr') # (data, column indices, row pointers) # doctest: +NORMALIZE_WHITESPACE # (array([ 4., -4., -4., 8., -4., -4., 8., -4., -4., 8., -4., -4., 4.])±1e-15, # array([0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 4, 3, 4])±1e-15, # array([ 0, 2, 5, 8, 11, 13])±1e-15) M.export('coo') # (data, (row indices, column indices)) # doctest: +NORMALIZE_WHITESPACE # (array([ 4., -4., -4., 8., -4., -4., 8., -4., -4., 8., -4., -4., 4.])±1e-15, # (array([0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4])±1e-15, # array([0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 4, 3, 4])±1e-15))  # Solvers Using topologies, bases and integrals, we now have the tools in place to start performing some actual functional-analytical operations. We start with what is perhaps the simplest of its kind, the least squares projection, demonstrating the different implementations now available to us and working our way up from there. Taking the geometry component \( x_0$$ as an example, to project it onto the basis $${φ_n}$$ means finding the coefficients $$\hat{u}_n$$ such that

$\left(\int_Ω φ_n φ_m \ dV\right) \hat u_m = \int_Ω φ_n x_0 \ dV$

for all $$φ_n$$, or $$A_{nm} \hat{u}_m = f_n$$. This is implemented as follows:

A = topo.integral('basis_m basis_n dV' @ ns, degree=2).eval()
f = topo.integral('basis_n x_0 dV' @ ns, degree=2).eval()
A.solve(f)
# solve > solving 5 dof system to machine precision using arnoldi solver
# solve > solver returned with residual 3e-17±1e-15
# array([0.  , 0.25, 0.5 , 0.75, 1.  ])±1e-15


Alternatively, we can write this in the slightly more general form

$R_n := \int_Ω φ_n (u - x_0) \ dV = 0.$

res = topo.integral('basis_n (u - x_0) dV' @ ns, degree=2)


Taking the derivative of $$R_n$$ to $$\hat{u}m$$ gives the above matrix $$A{nm}$$, and substituting for $$\hat{u}$$ the zero vector yields $$-f_n$$. Nutils can compute those derivatives for you, using the method Array.derivative() to compute the derivative with respect to an nutils.function.Argument, returning a new nutils.function.Array.

A = res.derivative('lhs').eval()
f = -res.eval(lhs=numpy.zeros(5))
A.solve(f)
# solve > solving 5 dof system to machine precision using arnoldi solver
# solve > solver returned with residual 3e-17±1e-15
# array([0.  , 0.25, 0.5 , 0.75, 1.  ])±1e-15


The above three lines are so common that they are combined in the function nutils.solver.solve_linear:

solver.solve_linear('lhs', res)
# solve > solving 5 dof system to machine precision using arnoldi solver
# solve > solver returned with residual 3e-17±1e-15
# array([0.  , 0.25, 0.5 , 0.75, 1.  ])±1e-15


We can take this formulation one step further. Minimizing

$S := \int_Ω (u - x_0)^2 \ dV$

for $$\hat{u}$$ is equivalent to the above two variants. The derivative of $$S$$ to $$\hat{u}_n$$ gives $$2 R_n$$:

sqr = topo.integral('(u - x_0)^2 dV' @ ns, degree=2)
solver.solve_linear('lhs', sqr.derivative('lhs'))
# solve > solving 5 dof system to machine precision using arnoldi solver
# solve > solver returned with residual 6e-17±1e-15
# array([0.  , 0.25, 0.5 , 0.75, 1.  ])±1e-15


The optimization problem can also be solved by the nutils.solver.optimize function, which has the added benefit that $$S$$ may be nonlinear in $$\hat{u}$$ --- a property not used here.

solver.optimize('lhs', sqr)
# optimize > solve > solving 5 dof system to machine precision using arnoldi solver
# optimize > solve > solver returned with residual 0e+00±1e-15
# optimize > optimum value 0.00e+00±1e-15
# array([0.  , 0.25, 0.5 , 0.75, 1.  ])±1e-15


Nutils also supports solving a partial optimization problem. In the Laplace problem stated above, the Dirichlet boundary condition at $$Γ_\text{left}$$ minimizes the following functional:

sqr = topo.boundary['left'].integral('(u - 0)^2 dS' @ ns, degree=2)


By passing the droptol argument, nutils.solver.optimize returns an array with nan ('not a number') for every entry for which the optimization problem is invariant, or to be precise, where the variation is below droptol:

cons = solver.optimize('lhs', sqr, droptol=1e-15)
# optimize > constrained 1/5 dofs
# optimize > optimum value 0.00e+00
cons
# array([ 0., nan, nan, nan, nan])±1e-15


Consider again the Laplace problem stated above. The residual is implemented as

res = topo.integral('∇_i(basis_n) ∇_i(u) dV' @ ns, degree=0)
res -= topo.boundary['right'].integral('basis_n dS' @ ns, degree=0)


Since this problem is linear in argument lhs, we can use the nutils.solver.solve_linear method to solve this problem. The constraints cons are passed via the keyword argument constrain:

lhs = solver.solve_linear('lhs', res, constrain=cons)
# solve > solving 4 dof system to machine precision using arnoldi solver
# solve > solver returned with residual 9e-16±1e-15
lhs
# array([0.  , 0.25, 0.5 , 0.75, 1.  ])±1e-15


For nonlinear residuals you can use nutils.solver.newton.

# Sampling

Having obtained the coefficient vector that solves the Laplace problem, we are now interested in visualizing the function it represents. Nutils does not provide its own post processing functionality, leaving that up to the preference of the user. It does, however, facilitate it, by allowing nutils.function.Array functions to be evaluated in samples. Bundling function values and a notion of connectivity, these form a bridge between Nutils' world of functions and the discrete realms of matplotlib, VTK, etc.

The Topology.sample(method, ...) method generates a collection of points on the nutils.topology.Topology, according to method. The 'bezier' method generates equidistant points per element, including the element vertices. The number of points per element per dimension is controlled by the second argument of Topology.sample(). An example:

bezier = topo.sample('bezier', 2)


The resulting nutils.sample.Sample object can be used to evaluate nutils.function.Array functions via the Sample.eval(func) method. To evaluate the geometry ns.x write

x = bezier.eval('x_0' @ ns)
x
# array([0.  , 0.25, 0.25, 0.5 , 0.5 , 0.75, 0.75, 1.  ])±1e-15


The first axis of the returned numpy.ndarray represents the collection of points. To reorder this into a sequence of lines in 1D, a triangulation in 2D or in general a sequence of simplices, use the Sample.tri attribute:

x.take(bezier.tri, 0)
# array([[0.  , 0.25],
#        [0.25, 0.5 ],
#        [0.5 , 0.75],
#        [0.75, 1.  ]])±1e-15


Now, the first axis represents the simplices and the second axis the vertices of the simplices.

If an nutils.function.Array function has arguments, those arguments must be specified by keyword arguments to Sample.eval(). For example, to evaluate ns.u with argument lhs replaced by solution vector lhs, obtained using nutils.solver.solve_linear above, write

u = bezier.eval('u' @ ns, lhs=lhs)
u
# array([0.  , 0.25, 0.25, 0.5 , 0.5 , 0.75, 0.75, 1.  ])±1e-15


We can now plot the sampled geometry x and solution u using matplotlib_, plotting each line in Sample.tri with a different color:

>>> plt.plot(x.take(bezier.tri.T, 0), u.take(bezier.tri.T, 0))


Recall that we have imported matplotlib.pyplot as plt above. The plt.plot() function takes an array of x-values and and array of y-values, both with the first axis representing vertices and the second representing separate lines, hence the transpose of bezier.tri.

The plt.plot() function also supports plotting lines with discontinuities, which are represented by nan values. We can use this to plot the solution as a single, but possibly discontinuous line. The function numpy.insert can be used to prepare a suitable array. An example:

nanjoin = lambda array, tri: numpy.insert(array.take(tri.flat, 0).astype(float),
slice(tri.shape[1], tri.size, tri.shape[1]), numpy.nan, axis=0)
nanjoin(x, bezier.tri)
# array([0.  , 0.25,  nan, 0.25, 0.5 ,  nan, 0.5 , 0.75,  nan, 0.75, 1.  ])±1e-15
plt.plot(nanjoin(x, bezier.tri), nanjoin(u, bezier.tri))


Note the difference in colors between the last two plots.

# 2D Laplace Problem

All of the above was written for a one-dimensional example. We now extend the Laplace problem to two dimensions and highlight the changes to the corresponding Nutils implementation. Let $$Ω$$ be a unit square with boundary $$Γ$$, on which the following boundary conditions apply:

$\begin{cases} u = 0 & Γ_\text{left} \\ \frac{∂u}{∂x_i} n_i = 0 & Γ_\text{bottom} \\ \frac{∂u}{∂x_i} n_i = \cos(1) \cosh(x_1) & Γ_\text{right} \\ u = \cosh(1) \sin(x_0) & Γ_\text{top} \end{cases}$

The 2D homogeneous Laplace solution is the field $$u$$ for which $$R(v, u) = 0$$ for all v, where

$R(v, u) := \int_Ω \frac{∂v}{∂x_i} \frac{∂u}{∂x_i} \ dV - \int_{Γ_\text{right}} v \cos(1) \cosh(x_1) \ dS.$

Adopting a Finite Element basis $${φ_n}$$ we obtain the discrete solution $$\hat{u}(x) = φ_n(x) \hat{u}_n$$ and the system of equations $$R(φ_n, \hat{u}) = 0$$.

Following the same steps as in the 1D case, a unit square mesh with 10x10 elements is formed using nutils.mesh.rectilinear:

nelems = 10
topo, geom = mesh.rectilinear([
numpy.linspace(0, 1, nelems+1), numpy.linspace(0, 1, nelems+1)])


Recall that nutils.mesh.rectilinear takes a list of element vertices per dimension. Alternatively you can create a unit square mesh using nutils.mesh.unitsquare, specifying the number of elements per dimension and the element type:

topo, geom = mesh.unitsquare(nelems, 'square')


The above two statements generate exactly the same topology and geometry. Try replacing 'square' with 'triangle' or 'mixed' to generate a unit square mesh with triangular elements or a mixture of square and triangular elements, respectively.

We start with a clean namespace, assign the geometry to ns.x, create a linear basis and define the solution ns.u as the contraction of the basis with argument lhs.

ns = Namespace()
ns.x = geom
ns.basis = topo.basis('std', degree=1)
ns.u = function.dotarg('lhs', ns.basis)


Note that the above statements are identical to those of the one-dimensional example.

The residual is implemented as

res = topo.integral('∇_i(basis_n) ∇_i(u) dV' @ ns, degree=2)
res -= topo.boundary['right'].integral('basis_n cos(1) cosh(x_1) dS' @ ns, degree=2)


The Dirichlet boundary conditions are rewritten as a least squares problem and solved for lhs, yielding the constraints vector cons:

sqr = topo.boundary['left'].integral('u^2 dS' @ ns, degree=2)
sqr += topo.boundary['top'].integral('(u - cosh(1) sin(x_0))^2 dS' @ ns, degree=2)
cons = solver.optimize('lhs', sqr, droptol=1e-15)
# optimize > solve > solving 21 dof system to machine precision using arnoldi solver
# optimize > solve > solver returned with residual 3e-17±2e-15
# optimize > constrained 21/121 dofs
# optimize > optimum value 4.32e-10±1e-9


To solve the problem res=0 for lhs subject to lhs=cons excluding the nan values, we can use nutils.solver.solve_linear:

lhs = solver.solve_linear('lhs', res, constrain=cons)
# solve > solving 100 dof system to machine precision using arnoldi solver
# solve > solver returned with residual 2e-15±2e-15


Finally, we plot the solution. We create a nutils.sample.Sample object from topo and evaluate the geometry and the solution:

bezier = topo.sample('bezier', 9)
x, u = bezier.eval(['x_i', 'u'] @ ns, lhs=lhs)


We use plt.tripcolor to plot the sampled x and u:

plt.tripcolor(x[:,0], x[:,1], bezier.tri, u, shading='gouraud', rasterized=True)
plt.colorbar()
plt.gca().set_aspect('equal')
plt.xlabel('x_0')
plt.ylabel('x_1')


This two-dimensional example is also available as the script examples/laplace.py.

# Release History

Nutils is developed on Github and released at cadence of roughly one year, with the actual time of release depending on the level of maturity of newly introduced features. Major releases introduce new features and may deprecate features that have been superceded. Minor releases contain only bugfixes and are always safe to upgrade to.

Every major release follows the following procedure:

• The development branch is branched off to release/x, where x is the major version number
• The release is assigned a code name, in alphabetical order, derived from a type of noodles dish
• The release commit is tagged as vx.0, with minor updates following as vx.1, vx.2 etc
• The package is uploaded to PyPi for easy installation using pip

## Development version

Since Nutils is under active development and releases are fairly infrequent, users may choose to work with the development version directly, taking for granted that their code may require continuous updating as features develop — keep an eye on the changelog in the project root! The development version is continuously updated here:

# Nutils 7 Hiyamugi

Nutils 7.0 was released on January 1st, 2022.

## What's New?

These are the main additions and changes since Nutils 6 Garak-Guksu.

### Expression and Namespace Version 2

The nutils.expression module has been renamed to nutils.expression_v1, the nutils.function.Namespace class to nutils.expression_v1.Namespace and the nutils.expression_v2 module has been added, featuring a new nutils.expression_v2.Namespace. The version 2 of the namespace v2 has an expression language that differs slightly from version 1, most notably in the way derivatives are written. The old namespace remains available for the time being. All examples are updated to the new namespace. You are encouraged to use the new namespace for newly written code.

### Changed: bifurcate has been replaced by spaces

In the past using functions on products of nutils.topology.Topology instances required using function.bifurcate. This has been replaced by the concept of 'spaces'. Every topology is defined in a space, identified by a name (str). Functions defined on some topology are considered constant on other topologies (defined on other spaces).

If you want to multiply two topologies, you have to make sure that the topologies have different spaces, e.g. via the space parameter of nutils.mesh.rectilinear. Example:

from nutils import mesh, function
Xtopo, x = mesh.rectilinear([4], space='X')
Ytopo, y = mesh.rectilinear([2], space='Y')
topo = Xtopo * Ytopo
geom = function.concatenate([x, y])


### Changed: function.Array shape must be constant

Resulting from to the function/evaluable split introduced in #574, variable length axes such as relating to integration points or sparsity can stay confined to the evaluable layer. In order to benefit from this situation and improve compatibility with Numpy's arrays, nutils.function.Array objects are henceforth limited to constant shapes. Additionally:

• The sparsity construct nutils.function.inflate has been removed;
• The nutils.function.Elemwise function requires all element arrays to be of the same shape, and its remaining use has been deprecated in favor of nutils.function.get;
• Aligning with Numpy's API, nutils.function.concatenate no longer automatically broadcasts its arguments, but instead demands that all dimensions except for the concatenation axis match exactly.

### Changed: locate arguments

The nutils.topology.Topology.locate method now allows tol to be left unspecified if eps is specified instead, which is repurposed as stop criterion for distances in element coordinates. Conversely, if only tol is specified, a corresponding minimal eps value is set automatically to match points near element edges. The ischeme and scale arguments are deprecated and replaced by maxdist, which can be left unspecified in general. The optional weights argument results in a sample that is suitable for integration.

### Moved: unit from types to separate module

The unit type has been moved into its own nutils.unit module, with the old location types.unit now holding a forward method. The forward emits a deprecation warning prompting to change nutils.types.unit.create (or its shorthand nutils.types.unit) to nutils.unit.create.

Libraries that are installed in odd locations will no longer be automatically located by Nutils (see b8b7a6d5 for reasons). Instead the user will need to set the appropriate environment variable, prior to starting Python. In Windows this is the PATH variable, in Linux and OS X LD_LIBRARY_PATH.

Crucially, this affects the MKL libraries when they are user-installed via pip. By default Nutils selects the best available matrix backend that it finds available, which could result in it silently falling back on Scipy or Numpy. To confirm that the path variable is set correctly run your application with matrix=mkl to force an error if MKL cannot be loaded.

### Function module split into function and evaluable

The function module has been split into a high-level, numpy-like function module and a lower-level evaluable module. The evaluable module is agnostic to the so-called points axis. Scripts that don't use custom implementations of function.Array should work without modification.

Custom implementations of the old function.Array should now derive from evaluable.Array. Furthermore, an accompanying implementation of function.Array should be added with a prepare_eval method that returns the former.

The following example implementation of an addition

class Add(function.Array):
def __init__(self, a, b):
super().__init__(args=[a, b], shape=a.shape, dtype=a.dtype)
def evalf(self, a, b):
return a+b


should be converted to

class Add(function.Array):
def __init__(self, a: function.Array, b: function.Array) -> None:
self.a = a
self.b = b
super().__init__(shape=a.shape, dtype=a.dtype)
def prepare_eval(self, **kwargs) -> evaluable.Array:
a = self.a.prepare_eval(**kwargs)
b = self.b.prepare_eval(**kwargs)

def __init__(self, a, b):
super().__init__(args=[a, b], shape=a.shape, dtype=a.dtype)
def evalf(self, a, b):
return a+b


### Solve multiple residuals to multiple targets

In problems involving multiple fields, where formerly it was required to nutils.function.chain the bases in order to construct and solve a block system, an alternative possibility is now to keep the residuals and targets separate and reference the several parts at the solving phase:

# old, still valid approach
ns.ubasis, ns.pbasis = function.chain([ubasis, pbasis])
ns.u_i = 'ubasis_ni ?dofs_n'
ns.p = 'pbasis_n ?dofs_n'

# new, alternative approach
ns.ubasis = ubasis
ns.pbasis = pbasis
ns.u_i = 'ubasis_ni ?u_n'
ns.p = 'pbasis_n ?p_n'

# common: problem definition
ns.σ_ij = '(u_i,j + u_j,i) / Re - p δ_ij'
ures = topo.integral('ubasis_ni,j σ_ij d:x d:x' @ ns, degree=4)
pres = topo.integral('pbasis_n u_,kk d:x' @ ns, degree=4)

# old approach: solving a single residual to a single target
dofs = solver.newton('dofs', ures + pres).solve(1e-10)

# new approach: solving multiple residuals to multiple targets
state = solver.newton(['u', 'p'], [ures, pres]).solve(1e-10)


In the new, multi-target approach, the return value is no longer an array but a dictionary that maps a target to its solution. If additional arguments were specified to newton (or any of the other solvers) then these are copied into the return dictionary so as to form a complete state, which can directly be used as an arguments to subsequent evaluations.

If an argument is specified for a solve target then its value is used as an initial guess (newton, minimize) or initial condition (thetamethod). This replaces the lhs0 argument which is not supported for multiple targets.

### New thetamethod argument deprecates target0

To explicitly refer to the history state in nutils.solver.thetamethod and its derivatives impliciteuler and cranknicolson, instead of specifiying the target through the target0 parameter, the new argument historysuffix specifies only the suffix to be added to the main target. Hence, the following three invocations are equivalent:

# deprecated
solver.impliciteuler('target', residual, inertia, target0='target0')
# new syntax
solver.impliciteuler('target', residual, inertia, historysuffix='0')
# equal, since '0' is the default suffix
solver.impliciteuler('target', residual, inertia)


### In-place modification of newton, minimize, pseudotime iterates

When nutils.solver.newton, nutils.solver.minimize or nutils.solver.pseudotime are used as iterators, the generated vectors are now modified in place. Therefore, if iterates are stored for analysis, be sure to use the .copy method.

### Deprecated function.elemwise

The function function.elemwise has been deprecated. Use function.Elemwise instead:

function.elemwise(topo.transforms, values) # deprecated
function.Elemwise(values, topo.f_index) # new


### Removed transforms attribute of bases

The transforms attribute of bases has been removed due to internal restructurings. The transforms attribute of the topology on which the basis was created can be used as a replacement:

reftopo = topo.refined
refbasis = reftopo.basis(...)
supp = refbasis.get_support(...)
#topo = topo.refined_by(refbasis.transforms[supp]) # no longer valid
topo = topo.refined_by(reftopo.transforms[supp]) # still valid


# Nutils 6 Garak-Guksu

Nutils 6.0 was released on April 29th, 2020.

Nutils 6.1 was released on July 17th, 2020.

Nutils 6.2 was released on October 7th, 2020.

Nutils 6.3 was released on November 18th, 2021.

## What's New?

These are the main additions and changes since Nutils 5 Farfalle.

### Sparse module

The new nutils.sparse module introduces a data type and a suite of manipulation methods for arbitrary dimensional sparse data. The existing integrate and integral methods now create data of this type under the hood, and then convert it to a scalar, Numpy array or nutils.matrix.Matrix upon return. To prevent this conversion and receive the sparse objects instead use the new nutils.sample.Sample.integrate_sparse or nutils.sample.eval_integrals_sparse.

### External dependency for parsing gmsh files

The nutils.mesh.gmsh method now depends on the external meshio module to parse .msh files:

python3 -m pip install --user --upgrade meshio


### Change dof order in basis.vector

When creating a vector basis using topo.basis(..).vector(nd), the order of the degrees of freedom changed from grouping by vector components to grouping by scalar basis functions:

[b0,  0]         [b0,  0]
[b1,  0]         [ 0, b0]
[.., ..] old     [b1,  0]
[bn,  0] ------> [ 0, b1]
[ 0, b0]     new [.., ..]
[.., ..]         [bn,  0]
[ 0, bn]         [ 0, bn]


This should not affect applications unless the solution vector is manipulated directly, such as might happen in unit tests. If required for legacy purposes the old vector can be retrieved using old = new.reshape(-1,nd).T.ravel(). Note that the change does not extend to nutils.function.vectorize.

### Change from stickybar to bottombar

For nutils.cli.run to draw a status bar, it now requires the external bottombar module to be installed:

python3 -m pip install --user bottombar


This replaces stickybar, which is no longer used. In addition to the log uri and runtime the status bar will now show the current memory usage, if that information is available. On Windows this requires psutil to be installed; on Linux and OSX it should work by default.

### Support for gmsh 'msh4' file format

The nutils.mesh.gmsh method now supports input in the 'msh4' file format, in addition to the 'msh2' format which remains supported for backward compatibility. Internally, nutils.mesh.parsegmsh now takes file contents instead of a file name.

### New command line option: gracefulexit

The new boolean command line option gracefulexit determines what happens when an exception reaches nutils.cli.run. If true (default) then the exception is handled as before and a system exit is initiated with an exit code of 2. If false then the exception is reraised as-is. This is useful in particular when combined with an external debugging tool.

### Log tracebacks at debug level

The way exceptions are handled by nutils.cli.run is changed from logging the entire exception and traceback as a single error message, to logging the exceptions as errors and tracebacks as debug messages. Additionally, the order of exceptions and traceback is fully reversed, such that the most relevant message is the first thing shown and context follows.

### Solve leniently to relative tolerance in Newton systems

The nutils.solver.newton method now sets the relative tolerance of the linear system to 1e-3 unless otherwise specified via linrtol. This is mainly useful for iterative solvers which can save computational effort by having their stopping criterion follow the current Newton residual, but it may also help with direct solvers to warn of ill conditioning issues. Iterations furthermore use nutils.matrix.Matrix.solve_leniently, thus proceeding after warning that tolerances have not been met in the hope that Newton convergence might be attained regardless.

### Linear solver arguments

The methods nutils.solver.newton, nutils.solver.minimize, nutils.solver.pseudotime, nutils.solver.solve_linear and nutils.solver.optimize now receive linear solver arguments as keyword arguments rather than via the solveargs dictionary, which is deprecated. To avoid name clashes with the remaining arguments, argument names must be prefixed by lin:

solver.solve_linear('lhs', res,
solveargs=dict(solver='gmres')) # deprecated syntax

solver.solve_linear('lhs', res,
linsolver='gmres') # new syntax


### Iterative refinement

Direct solvers enter an iterative refinement loop in case the first pass did not meet the configured tolerance. In machine precision mode (atol=0, rtol=0) this refinement continues until the residual stagnates.

### Matrix solver tolerances

The absolute and/or relative tolerance for solutions of a linear system can now be specified in nutils.matrix.Matrix.solve via the atol resp. rtol arguments, regardless of backend and solver. If the backend returns a solution that violates both tolerances then an exception is raised of type nutils.matrix.ToleranceNotReached, from which the solution can still be obtained via the .best attribute. Alternatively the new method nutils.matrix.Matrix.solve_leniently always returns a solution while logging a warning if tolerances are not met. In case both tolerances are left at their default value or zero then solvers are instructed to produce a solution to machine precision, with subsequent checks disabled.

### Use stringly for command line parsing

Nutils now depends on stringly (version 1.0b1) for parsing of command line arguments. The new implementation of nutils.cli.run is fully backwards compatible, but the preferred method of annotating function arguments is now as demonstrated in all of the examples.

For new Nutils installations Stringly will be installed automatically as a dependency. For existing setups it can be installed manually as follows:

python3 -m pip install --user --upgrade stringly


### Fixed and fallback lengths in (namespace) expressions

The nutils.function.Namespace has two new arguments: length_<indices> and fallback_length. The former can be used to assign fixed lengths to specific indices in expressions, say index i should have length 2, which is used for verification and resolving undefined lengths. The latter is used to resolve remaining undefined lengths:

ns = nutils.function.Namespace(length_i=2, fallback_length=3)
ns.eval_ij('δ_ij') # using length_i
# Array<2,2>
ns.eval_jk('δ_jk') # using fallback_length
# Array<3,3>


### Treelog update

Nutils now depends on treelog version 1.0b5, which brings improved iterators along with other enhancements. For transitional convenience the backwards incompatible changes have been backported in the nutils.log wrapper, which now emits a warning in case the deprecated methods are used. This wrapper is scheduled for deletion prior to the release of version 6.0. To update treelog to the most recent version use:

python -m pip install -U treelog


### Unit type

The new nutils.types.unit allows for the creation of a unit system for easy specification of physical quantities. Used in conjunction with nutils.cli.run this facilitates specifying units from the command line, as well as providing a warning mechanism against incompatible units:

U = types.unit.create(m=1, s=1, g=1e-3, N='kg*m/s2', Pa='N/m2')
def main(length=U('2m'), F=U('5kN')):
topo, geom = mesh.rectilinear([numpy.linspace(0,length,10)])

python myscript.py length=25cm # OK
python myscript.py F=10Pa # error!


### Sample basis

Samples now provide a nutils.sample.Sample.basis: an array that for any point in the sample evaluates to the unit vector corresponding to its index. This new underpinning of nutils.sample.Sample.asfunction opens the way for sampled arguments, as demonstrated in the last example below:

H1 = mysample.asfunction(mydata) # mysample.eval(H1) == mydata
H2 = mysample.basis().dot(mydata) # mysample.eval(H2) == mydata
ns.Hbasis = mysample.basis()
H3 = 'Hbasis_n ?d_n' @ ns # mysample.eval(H3, d=mydata) == mydata


### Higher order gmsh geometries

Gmsh element support has been extended to include cubic and quartic meshes in 2D and quadratic meshes in 3D, and parsing the msh file is now a cacheable operation. Additionally, tetrahedra now define bezier points at any order.

### Repository location

The Nutils repository has moved to https://github.com/evalf/nutils.git. For the time being the old address is maintained by Github as an alias, but in the long term you are advised to update your remote as follows:

git remote set-url origin https://github.com/evalf/nutils.git


# Nutils 5 Farfalle

Nutils 5.0 was released on April 3rd, 2020.

Nutils 5.1 was released on September 3rd, 2019.

Nutils 5.2 was released on June 11th, 2019.

## What's New?

These are the main additions and changes since Nutils 4 Eliche.

### Matrix matmul operator, solve with multiple right hand sides

The Matrix.matvec method has been deprecated in favour of the new __matmul__ (@) operator, which supports multiplication arrays of any dimension. The nutils.matrix.Matrix.solve method has been extended to support multiple right hand sides:

matrix.matvec(lhs) # deprecated
matrix @ lhs # new syntax
matrix @ numpy.stack([lhs1, lhs2, lhs3], axis=1)
matrix.solve(rhs)
matrix.solve(numpy.stack([rhs1, rhs2, rhs3], axis=1)


### MKL's fgmres method

Matrices produced by the MKL backend now support the nutils.matrix.Matrix.solve argument solver='fmgres' to use Intel MKL's fgmres method.

### Thetamethod time target

The nutils.solver.thetamethod class, as well as its special cases impliciteuler and cranknicolson, now have a timetarget argument to specify that the formulation contains a time variable:

res = topo.integral('...?t... d:x' @ ns, degree=2)
solver.impliciteuler('dofs', res, ..., timetarget='t')


### New leveltopo argument for trimming

In nutils.topology.Topology.trim, in case the levelset cannot be evaluated on the to-be-trimmed topology itself, the correct topology can now be specified via the new leveltopo argument.

### New unittest assertion assertAlmostEqual64

nutils.testing.TestCase now facilitates comparison against base64 encoded, compressed, and packed data via the new method nutils.testing.TestCase.assertAlmostEqual64. This replaces numeric.assert_allclose64 which is now deprecated and scheduled for removal in Nutils 6.

### Fast locate for structured topology, geometry

A special case nutils.topology.Topology.locate method for structured topologies checks of the geometry is an affine transformation of the natural configuration, in which case the trivial inversion is used instead of expensive Newton iterations:

topo, geom = mesh.rectilinear([2, 3])
smp = topo.locate(geom/2-1, [[-.1,.2]])
# locate detected linear geometry: x = [-1. -1.] + [0.5 0.5] xi ~+2.2e-16


### Lazy references, transforms, bases

The introduction of sequence abstractions nutils.elementseq and nutils.transformseq, together with and a lazy implementation of nutils.function.Basis basis functions, help to prevent the unnecessary generation of data. In hierarchically refined topologies, in particular, this results in large speedups and a much reduced memory footprint.

### Switch to treelog

The nutils.log module is deprecated and will be replaced by the externally maintained treelog <https://github.com/evalf/treelog>_, which is now an installation dependency.

### Replace pariter, parmap by fork, range.

The nutils.parallel module is largely rewritten. The old methods pariter and parmap are replaced by the nutils.parallel.fork context, combined with the shared nutils.parallel.range iterator:

indices = parallel.range(10)
with parallel.fork(nprocs=2) as procid:
for index in indices:
print('procid={}, index={}'.format(procid, index))


# Nutils 4 Eliche

Nutils 4.0 was released on June 11th, 2019.

Nutils 4.1 was released on August 28th, 2018.

## What's New?

These are the main additions and changes since Nutils 3 Dragon Beard.

### Spline basis continuity argument

In addition to the knotmultiplicities argument to define the continuity of basis function on structured topologies, the nutils.topology.Topology.basis method now supports the continuity argument to define the global continuity of basis functions. With negative numbers counting backwards from the degree, the default value of -1 corresponds to a knot multiplicity of 1.

### Eval arguments

Functions of type nutils.function.Evaluable can receive arguments in addition to element and points by depending on instances of nutils.function.Argument and having their values specified via nutils.sample.Sample.eval:

f = geom.dot(function.Argument('myarg', shape=geom.shape))
f = 'x_i ?myarg_i' @ ns # equivalent operation in namespace
topo.sample('uniform', 1).eval(f, myarg=numpy.ones(geom.shape))


### The d:-operator

Namespace expression syntax now includes the d: Jacobian operator, allowing one to write 'd:x' @ ns instead of function.J(ns.x). Since including the Jacobian in the integrand is preferred over specifying it separately, the geometry argument of nutils.topology.Topology.integrate is deprecated:

topo.integrate(ns.f, geometry=ns.x) # deprecated
topo.integrate(ns.f * function.J(ns.x)) # was and remains valid
topo.integrate('f d:x' @ ns) # new namespace syntax


### Truncated hierarchical bsplines

Hierarchically refined topologies now support basis truncation, which reduces the supports of individual basis functions while maintaining the spanned space. To select between truncated and non-truncated the basis type must be prefixed with 'th-' or 'h-', respectively. A non-prefixed basis type falls back on the default implementation that fails on all types but discont:

htopo.basis('spline', degree=2) # no longer valid
htopo.basis('h-spline', degree=2) # new syntax for original basis
htopo.basis('th-spline', degree=2) # new syntax for truncated basis
htopo.basis('discont', degree=2) # still valid


### Transparent function cache

The nutils.cache module provides a memoizing function decorator nutils.cache.function which reads return values from cache in case a set of function arguments has been seen before. It is similar in function to Python's functools.lru_cache, except that the cache is maintained on disk and nutils.types.nutils_hash is used to compare arguments, which means that arguments need not be Python hashable. The mechanism is activated via nutils.cache.enable:

@cache.function
def f(x):
return x * 2

with cache.enable():
f(10)


If nutils.cli.run is used then the cache can also be enabled via the new --cache command line argument. With many internal Nutils functions already decorated, including all methods in the nutils.solver module, transparent caching is available out of the box with no further action required.

### New module: types

The new nutils.types module unifies and extends components relating to object types. The following preexisting objects have been moved to the new location:

• util.enforcetypestypes.apply_annotations
• util.frozendicttypes.frozendict
• numeric.consttypes.frozenarray

### MKL matrix, Pardiso solver

The new MKL backend generates matrices that are powered by Intel's Math Kernel Library, which notably includes the reputable Pardiso solver. This requires libmkl to be installed, which is conveniently available through pip:

pip install mkl


When nutils.cli.run is used the new matrix type is selected automatically if it is available, or manually using --matrix=MKL.

### Nonlinear minimization

For problems that adhere to an energy structure, the new solver method nutils.solver.minimize provides an alternative mechanism that exploits this structure to robustly find the energy minimum:

res = sqr.derivative('dofs')
solver.newton('dofs', res, ...)
solver.minimize('dofs', sqr, ...) # equivalent


### Data packing

Two new methods, nutils.numeric.pack and its inverse nutils.numeric.unpack, provide lossy compression to floating point data. Primarily useful for regression tests, the convenience method numeric.assert_allclose64 combines data packing with zlib compression and base64 encoding for inclusion in Python codes.

# Nutils 3 Dragon Beard

Nutils 3.0 was released on August 22nd, 2018.

Nutils 3.1 was released on February 5th, 2018.

## What's New?

These are the main additions and changes since Nutils 2 Chuka Men.

### New: function.Namespace

The nutils.function.Namespace object represents a container of nutils.function.Array instances:

ns = function.Namespace()
ns.x = geom
ns.basis = domain.basis('std', degree=1).vector(2)


In addition to bundling arrays, arrays can be manipulated using index notation via string expressions using the nutils.expression syntax:

ns.sol_i = 'basis_ni ?dofs_n'
f = ns.eval_i('sol_i,j n_j')


### New: Topology.integral

Analogous to nutils.topology.Topology.integrate, which integrates a function and returns the result as a (sparse) array, the new method nutils.topology.Topology.integral with identical arguments results in an nutils.sample.Integral object for postponed evaluation:

x = domain.integrate(f, geometry=geom, degree=2) # direct
integ = domain.integral(f, geometry=geom, degree=2) # indirect
x = integ.eval()


Integral objects support linear transformations, derivatives and substitutions. Their main use is in combination with routines from the nutils.solver module.

### Removed: TransformChain, CanonicalTransformChain

Transformation chains (sequences of transform items) are stored as standard tuples. Former class methods are replaced by module methods:

elem.transform.promote(ndims) # no longer valid
transform.promote(elem.transform, ndims) # new syntax


In addition, every edge_transform and child_transform of Reference objects is changed from (typically unit-length) TransformChain to nutils.transform.TransformItem.

### Changed: command line interface

Command line parsers nutils.cli.run or nutils.cli.choose dropped support for space separated arguments (--arg value), requiring argument and value to be joined by an equals sign instead:

python script.py --arg=value


Boolean arguments are specified by omitting the value and prepending 'no' to the argument name for negation:

python script.py --pdb --norichoutput


python script.py arg=value pdb norichoutput


### New: Topology intersections (deprecates common_refinement)

Intersections between topologies can be made using the & operator. In case the operands have different refinement patterns, the resulting topology will consist of the common refinements of the intersection:

intersection = topoA & topoB
interface = topo['fluid'].boundary & ~topo['solid'].boundary


### Changed: Topology.indicator

The nutils.topology.Topology.indicator method is moved from subtopology to parent topology, i.e. the topology you want to evaluate the indicator on, and now takes the subtopology is an argument:

ind = domain.boundary['top'].indicator() # no longer valid ind = domain.boundary.indicator(domain.boundary['top']) # new syntax ind = domain.boundary.indicator('top') # equivalent shorthand

### Changed: Evaluable.eval

The nutils.function.Evaluable.eval method accepts a flexible number of keyword arguments, which are accessible to evalf by depending on the EVALARGS token. Standard keywords are _transforms for transformation chains, _points for integration points, and _cache for the cache object:

f.eval(elem, 'gauss2') # no longer valid
ip, iw = elem.getischeme('gauss2')
tr = elem.transform, elem.opposite
f.eval(_transforms=tr, _points=ip) # new syntax


### New: numeric.const

The numeric.const array represents an immutable, hashable array:

A = numeric.const([[1,2],[3,4]])
d = {A: 1}


Existing arrays can be wrapped into a const object by adding copy=False. The writeable flag of the original array is set to False to prevent subsequent modification:

A = numpy.array([1,2,3])
Aconst = numeric.const(A, copy=False)
A[1] = 4
# ValueError: assignment destination is read-only


### New: function annotations

The util.enforcetypes decorator applies conversion methods to annotated arguments:

@util.enforcetypes
def f(a:float, b:tuple)
print(type(a), type(b))
f(1, [2])
# <class 'float'> <class 'tuple'>


The decorator is by default active to constructors of cache.Immutable derived objects, such as function.Evaluable.

### Changed: Evaluable._edit

Evaluable objects have a default edit implementation that re-instantiates the object with the operand applied to all constructor arguments. In situations where the default implementation is not sufficient it can be overridden by implementing the edit method (note: without the underscore):

class B(function.Evaluable):
def __init__(self, d):
assert isinstance(d, dict)
self.d = d
def edit(self, op):
return B({key: op(value) for key, value in self.d.items()})


### Changed: function derivatives

The nutils.function.derivative axes argument has been removed; derivative(func, var) now takes the derivative of func to all the axes in var:

der = function.derivative(func, var,
axes=numpy.arange(var.ndim)) # no longer valid
der = function.derivative(func, var) # new syntax


### New module: cli

The nutils.util.run function is deprecated and replaced by two new functions, nutils.cli.choose and nutils.cli.run. The new functions are very similar to the original, but have a few notable differences:

• cli.choose requires the name of the function to be executed (typically 'main'), followed by any optional arguments
• cli.run does not require the name of the function to be executed, but only a single one can be specified
• argument conversions follow the type of the argument's default value, instead of the result of eval
• the --tbexplore option for post-mortem debugging is replaced by --pdb, replacing Nutils' own traceback explorer by Python's builtin debugger
• on-line debugging is provided via the ctrl+c signal handler
• function annotations can be used to describe arguments in both help messages and logging output (see examples)

### New module: solver

The nutils.solver module provides infrastructure to facilitate formulating and solving complicated nonlinear problems in a structured and largely automated fashion.

### New: topology.with{subdomain,boundary,interfaces,points}

Topologies have been made fully immutable, which means that the old setitem operation is no longer supported. Instead, to add a subtopology to the domain, its boundary, its interfaces, or points, any of the methods withsubdomain, withboundary, withinterfaces, and withpoints, respectively, will return a copy of the topology with the desired groups added:

topo.boundary['wall'] = topo.boundary['left,top'] # no longer valid
newtopo = topo.withboundary(wall=topo.boundary['left,top']) # new syntax
newtopo = topo.withboundary(wall='left,top') # equivalent shorthand
newtopo.boundary['wall'].integrate(...)


### New: circular symmetry

Any topology can be revolved using the new nutils.topology.Topology.revolved method, which interprets the first geometry dimension as a radius and replaces it by two new dimensions, shifting the remaining axes backward. In addition to the modified topology and geometry, simplifying function is returned as the third return value which replaces all occurrences of the revolution angle by zero. This should only be used after all gradients have been computed:

rdomain, rgeom, simplify = domain.revolved(geom)
basis = rdomain.basis('spline', degree=2)
rdomain.integrate(M, geometry=rgeom, ischeme='gauss2', edit=simplify)


### Renamed mesh.gmesh to mesh.gmsh; added support for periodicity

The gmsh importer was unintentionally misnamed as gmesh; this has been fixed. With that the old name is deprecated and will be removed in future. In addition, support for the non-physical mesh format and externally supplied boundary labels has been removed (see the unit test tests/mesh.py for examples of valid .geo format). Support is added for periodicity and interface groups.

# Nutils 2 Chuka Men

Nutils 2.0 was released on February 18th, 2016.

## What's New?

These are the main additions and changes since Nutils 1 Bakmi.

### Changed: jump sign

The jump operator has been changed according to the following definition: jump(f) = opposite(f) - f. In words, it represents the value of the argument from the side that the normal is pointing toward, minus the value from the side that the normal is pointing away from. Compared to the old definition this means the sign is flipped.

### Changed: Topology objects

The Topology base class no longer takes a list of elements in its constructor. Instead, the __iter__ method should be implemented by the derived class, as well as __len__ for the number of elements, and getelem(index) to access individual elements. The 'elements' attribute is deprecated.

The nutils.topology.StructuredTopology object no longer accepts an array with elements. Instead, an 'axes' argument is provided with information that allows it to generate elements in the fly. The 'structure' attribute is deprecated. A newly added shape tuple is now a documented attribute.

### Changed: properties dumpdir, outdir, outrootdir

Two global properties have been renamed as follows:

• dumpdir → outdir
• outdir → outrootdir

The outrootdir defaults to ~/public_html and can be redefined from the command line or in the .nutilsrc configuration file. The outdir defaults to the current directory and is redefined by util.run, nesting the name/date/time subdirectory sequence under outrootdir.

### Changed: sum axis argument

The behaviour of nutils.function.sum is inconsistent with that of the Numpy counterparts. In case no axes argument is specified, Numpy sums over all axes, whereas Nutils sums over the last axis. To undo this mistake and transition to Numpy's behaviour, calling sum without an axes argument is deprecated and will be forbidden in Nutils 3.0. In Nutils 4.0 it will be reintroduced with the corrected meaning.

### Changed: strict dimension equality in function.outer

The nutils.function.outer method allows arguments of different dimension by left-padding the smallest prior to multiplication. There is no clear reason for this generality and it hinders error checking. Therefore in future in function.outer(a, b), a.ndim must equal b.ndim. In a brief transition period non-equality emits a warning.

### Changed: Evaluable base class

Relevant only for custom nutils.function.Evaluable objects, the evalf method changes from constructor argument to instance/class method:

class MyEval( function.Evaluable):
def __init__(self, ...):
function.Evaluable(args=[...], shape=...)
def evalf( self, ...):
...


Moreover, the args argument may only contain Evaluable objects. Static information is to be passed through self.

### Removed: _numeric C-extension

At this point Nutils is pure Python. It is no longer necessary to run make to compile extension modules. The numeric.py module remains unchanged.

### Periodic boundary groups

Touching elements of periodic domains are no longer part of the boundary topology. It is still available as boundary of an appropriate non-periodic subtopology:

domain.boundary['left'] # no longer valid
domain[:,:1].boundary['left'] # still valid


### New module: transform

The new nutils.transform module provides objects and operations relating to affine coordinate transformations.

### Traceback explorer disabled by default

The new command line switch --tbexplore activates the traceback explorer on program failure. To change the default behavior add tbexplore=True to your .nutilsrc file.

### Rich output

The new command line switch --richoutput activates color and unicode output. To change the default behavior add richoutput=True to your .nutilsrc file.

# Nutils 1 Bakmi

Nutils 1.0 was released on August 4th, 2014.

# Nutils 0 Anelli

Nutils 0.0 was released on October 28th, 2013.

# Examples

The fastest way to build a new Nutils simulation is to borrow bits and pieces from existing scripts. Aiming to facilitate this practice, the following website provides an overview of concise examples demonstrating different areas of physics and varying computational techniques:

The examples are taken both from the nutils repository and from user contributed repositories, and are tested regularly to confirm validity against different versions of Nutils.

## Contributing

Users are encouraged to contribute (concise versions of) their simulations to this collection of examples. In doing so, they help other users get up to speed, they help the developers by adding to a large body of realistic codes to test Nutils against, and, in doing so, they may even help themselves by preventing future Nutils version from accidentally breaking their code.

Examples are submitted by means of a pull request to the examples repository, which should add a yaml file to the examples/user directory. The file should define the following entries:

• name — Title of the simulation.
• authors — List of author names.
• description — Markdown formatted description of the simulation.
• repository — URL of the Git repository that contains the script.
• commit — Commit hash.
• script — Path of the script.
• images — List of images that are selected as preview.
• tags — List of relevant tags.

Once merged, the script becomes part of the automated testing suite which runs it at regular intervals against the latest Nutils version. The code itself remains hosted on the external git repository. In case new features merit updates to the script, the developers may reach out with concrete suggestions to keep the examples relevant.

# Science

Nutils has been used in scientific publications since its earliest releases, such as this 2015 analysis of a trabecular bone fragment by Verhoosel et al, combining several of Nutils' strengths including the Finite Cell Method, hierarchical refinement, and isogeometric analysis. One of its images was later selected to feature as cover art for the Encyclopedia of Computational Mechanics.

Nutils has since been used in a wide range of applications, pushing the boundaries of computational techniques, studying physical phenomena, and testing new models. The publication overview lists an up to date selection of Nutils powered research. If you are using Nutils in your own research, please consider citing Nutils in your publications.

# Publications

Below is an overview of mostly peer reviewed articles that use Nutils for numerical experiments. In case your Nutils powered research is not listed here, please send the DOI to info@nutils.org or submit a pull request with the new entry. Articles that cite Nutils will be picked up automatically.

# Citing Nutils

To acknowledge Nutils in publications, authors are encouraged to cite the specific version of Nutils that was used to generate their results. To this end, Nutils releases are assigned a Digital Object Identifier (DOI) by Zenodo which can be used for citations. For instance, a bibliography entry for Nutils 7.0 could look like this:

For LaTeX documents, the corresponding bibtex entry would be:

@misc{nutils7,
title = {Nutils 7.0},
author = {van Zwieten, J.S.B. and van Zwieten, G.J. and Hoitinga, W.},
publisher = {Zenodo},
year = {2022},
doi = {10.5281/zenodo.6006701},
}


Note that Zenodo can additionally host and assign a DOI to code that is specific to the publication, which is a great way to share digital artifacts and ensure reproducibility of the research.

# Support

For questions that are not answered by the tutorial, the API reference for the relevant release, or the examples, there are a few avenues for getting additional support.

Questions that lend themselves to be formulated in a concise and general way can be made into a Q&A topic, where both developers and advanced users can weigh in their answers, and where they may be of benefit to others encountering the same issue. Be sure to check first if your issue was not discussed already!

If you believe that you have spotted a bug, the best thing to do is to file an issue. Issues should contain a description of the problem, the expected behaviour, and steps to reproduce, including the version of Nutils that the issue relates to. If you believe that the bug was recently introduced you can help the developers by identifying the first failing commit, for instance using bisection.

Finally, for general discussions, questions, suggestions, or just to say hello, everybody is welcome to join the nutils-users support channel at #nutils-users:matrix.org. Note that you will need to create an account at any Matrix server in order to join this channel.