https://github.com/alan-turing-institute/stat-fem

Python tools for solving data-constrained finite element problems
https://github.com/alan-turing-institute/stat-fem

finite-element-analysis hut23 hut23-183 probabilistic-numerics uncertainty-quantification

Last synced: 5 months ago
JSON representation

Python tools for solving data-constrained finite element problems

• Host: GitHub
• URL: https://github.com/alan-turing-institute/stat-fem
• Owner: alan-turing-institute
• License: lgpl-3.0
• Created: 2019-11-12T10:23:05.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-11-09T14:28:44.000Z (over 3 years ago)
• Last Synced: 2024-03-20T20:58:20.023Z (about 1 year ago)
• Topics: finite-element-analysis, hut23, hut23-183, probabilistic-numerics, uncertainty-quantification
• Language: Python
• Size: 727 KB
• Stars: 11
• Watchers: 6
• Forks: 6
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# stat-fem

Python tools for solving data-constrained finite element problems

[![Build Status](https://travis-ci.com/alan-turing-institute/stat-fem.svg?branch=master)](https://travis-ci.com/alan-turing-institute/stat-fem)

[![codecov](https://codecov.io/gh/alan-turing-institute/stat-fem/branch/master/graph/badge.svg)](https://codecov.io/gh/alan-turing-institute/stat-fem)

[![Documentation Status](https://readthedocs.org/projects/stat-fem/badge/?version=latest)](https://stat-fem.readthedocs.io/en/latest/?badge=latest)

## Overview

This package provides a Python implementation of the Statistical Finite Element Method (FEM) as

described in the paper by Girolami et al. [1] to use data observations to constrain

FEM models. The package builds on top of Firedrake [2] to assemble the underlying FEM system

and uses PETSc [3-4] to perform the sparse linear algebra routines. These tools should allow

the user to create efficient, scalable solvers based on high level Python code to address

challenging problems in data-driven numerical analysis.

## Installation

### Installing stat-fem

`stat-fem` requires a working Firedrake installation. The easiest way

to obtain Firedrake is to use the installation script provided by the

Firedrake project on the [firedrake homepage](https://www.firedrakeproject.org).

```bash

curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/master/scripts/firedrake-install

python3 firedrake-install --install git+https://github.com/alan-turing-institute/stat-fem#egg=stat-fem

```

This will install Firedrake and install the `stat-fem` library inside the

Firedrake virtual environment. If this does not work, details on manual

installation are provided in the [documentation](https://stat-fem.readthedocs.io/en/latest/intro/installation.html).

### Using a Docker Container

Alternatively, we provide a working Firedrake Docker container that has the `stat-fem` code

and dependencies installed within the Firedrake virtual environment. See the `docker`

directory in the `stat-fem` repository.

### Testing the installation

The code comes with a full suite of unit tests. Running the test suite uses pytest and pytest-mpi

to collect and run the tests. To run the tests on a single process, simply enter `pytest` into

the running virtual environment from any location in the stat-fem directory. To run the test

suite in parallel, enter `mpiexec -n 2 python -m pytest --with-mpi` or

`mpiexec -n 4 python -m pytest --with-mpi` depending on the number of desired

processes to be used. Tests have only been written for 2 and 4 processes, so

you may get a failure if you attempt to use other choices for the number

of processes.

## Example Scripts

An example illustrating the various code capabilities and features is included in

the `stat-fem/examples` directory.

## Contact

This software was written by Eric Daub as part of a project with the Research Engineering Group at the

Alan Turing Institute.

Any bugs or issues should be filed in the issue tracker on the main Github page.

## References

[1] Mark Girolami, Eky Febrianto, Ge Yin, and Fehmi Cirak. The

    statistical finite element method (statFEM) for coherent synthesis

    of observation data and model predictions. *Computer Methods in

    Applied Mechanics and Engineering*, Volume 375, 2021, 113533,

    https://doi.org/10.1016/j.cma.2020.113533.

[2] Florian Rathgeber, David A. Ham, Lawrence Mitchell, Michael Lange, Fabio Luporini,

    Andrew T. T. Mcrae, Gheorghe-Teodor Bercea, Graham R. Markall, and Paul H. J. Kelly.

    Firedrake: automating the finite element method by composing abstractions. *ACM Trans.

    Math. Softw.*, 43(3):24:1–24:27, 2016. URL: http://arxiv.org/abs/1501.01809,

    arXiv:1501.01809, doi:10.1145/2998441.

[3] L. Dalcin, P. Kler, R. Paz, and A. Cosimo, Parallel Distributed Computing using Python,

    Advances in Water Resources, 34(9):1124-1139, 2011.

    http://dx.doi.org/10.1016/j.advwatres.2011.04.013

[4] S. Balay, S. Abhyankar, M. Adams, J. Brown, P. Brune, K. Buschelman, L. Dalcin, A. Dener,

    V. Eijkhout, W. Gropp, D. Karpeyev, D. Kaushik, M. Knepley, D. May, L. Curfman McInnes,

    R. Mills, T. Munson, K. Rupp, P. Sanan, B. Smith, S. Zampini, H. Zhang, and H. Zhang,

    PETSc Users Manual, ANL-95/11 - Revision 3.12, 2019.

    http://www.mcs.anl.gov/petsc/petsc-current/docs/manual.pdf