hIPPYlib
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems - Published in JOSS (2018)
Science Score: 100.0%
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✓CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
✓Committers with academic emails
4 of 15 committers (26.7%) from academic institutions -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Repository
An Extensible Software Framework for Large-Scale Inverse Problems
Basic Info
- Host: GitHub
- Owner: hippylib
- License: gpl-2.0
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://hippylib.github.io
- Size: 2.63 MB
Statistics
- Stars: 146
- Watchers: 14
- Forks: 48
- Open Issues: 5
- Releases: 17
Metadata Files
README.md
Inverse Problem PYthon library
```
/ | / |/ \ / \ / \ / |/ |/ |/ |
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$$$$$$$ | $$ | $$ $$/ $$ $$/ $$ $$/ $$ |$$ |$$$$$$$ |
$$ | $$ | $$ | $$$$$$$/ $$$$$$$/ $$$$/ $$ |$$ |$$ | $$ |
$$ | $$ | $$ | $$ | $$ | $$ | $$ |$$ |$$ |__$$ |
$$ | $$ |/ $$ |$$ | $$ | $$ | $$ |$$ |$$ $$/
$$/ $$/ $$$$$$/ $$/ $$/ $$/ $$/ $$/ $$$$$$$/
```
https://hippylib.github.io
hIPPYlib implements state-of-the-art scalable algorithms for
deterministic and Bayesian inverse problems governed by partial differential equations (PDEs).
It builds on FEniCS
(a parallel finite element element library) for the discretization of the PDE
and on PETSc for scalable and efficient linear
algebra operations and solvers.
For building instructions, see the file INSTALL.md. Copyright information
and licensing restrictions can be found in the file COPYRIGHT.
The best starting point for new users interested in hIPPYlib's
features are the interactive tutorials in the tutorial folder.
Conceptually, hIPPYlib can be viewed as a toolbox that provides the
building blocks for experimenting new ideas and developing scalable
algorithms for PDE-constrained deterministic and Bayesian inverse problems.
In hIPPYlib the user can express the forward PDE and the likelihood in
weak form using the friendly, compact, near-mathematical notation of
FEniCS, which will then automatically generate efficient code for the
discretization. Linear and nonlinear, and stationary and
time-dependent PDEs are supported in hIPPYlib.
For stationary problems, gradient and Hessian information can be
automatically generated by hIPPYlib using FEniCS symbolic differentiation
of the relevant weak forms. For time-dependent problems, instead, symbolic
differentiation can only be used for the spatial terms, and the contribution
to gradients and Hessians arising from the time dynamics needs to be provided
by the user.
Noise and prior covariance operators are modeled as inverses of elliptic differential operators allowing us to build on existing fast multigrid solvers for elliptic operators without explicitly constructing the dense covariance operator.
The key property of the algorithms underlying hIPPYlib is that solution
of the deterministic and Bayesian inverse problem is computed
at a cost, measured in forward PDE solves, that is independent of the
parameter dimension.
hIPPYlib provides a robust implementation of the inexact
Newton-conjugate gradient algorithm to compute the maximum a posterior
(MAP) point. The gradient and Hessian actions are
computed via their weak form specification in FEniCS by
constraining the state and adjoint variables to satisfy the forward
and adjoint problem. The Newton system is solved inexactly by early
termination of CG iterations via Eisenstat-Walker (to prevent
oversolving) and Steihaug (to avoid negative curvature)
criteria. Two globalization techniques are available to the user:
Armijo back-tracking line search and trust region.
In hIPPYlib, the posterior covariance is approximated by the
inverse of the Hessian of the negative log posterior evaluated at
the MAP point. This Gaussian approximation is exact when the
parameter-to-observable map is linear; otherwise, its logarithm agrees
to two derivatives with the log posterior at the MAP point, and thus it
can serve as a proposal for Hessian-based Markov chain Monte Carlo (MCMC)
methods. hIPPYlib makes the construction of the posterior covariance
tractable by invoking a low-rank approximation of the Hessian of the
log likelihood.
hIPPYlib also offers scalable methods for sample generation.
To sample large scale spatially correlated Gaussian random fields from the prior
distribution, hIPPYlib implements a new method that strongly relies on the
structure of the covariance operator defined as the inverse of a differential operator:
by exploiting the assembly procedure of finite element matrices hIPPYlib constructs a sparse Cholesky-like rectangular decomposition of the precision operator.
To sample from a local Gaussian approximation to the posterior (such as at the MAP point)
hIPPYlib exploits the low rank factorization of the Hessian of the
log likelihood to correct samples from the prior distribution.
Finally, to explore the posterior distribution, hIPPYlib implements
dimension independent MCMC sampling methods enchanted by Hessian information.
Finally, randomized and probing algorithms are available to compute the pointwise variance of the prior/posterior distribution and the trace of the covariance operator.
Owner
- Name: hippylib
- Login: hippylib
- Kind: organization
- Repositories: 20
- Profile: https://github.com/hippylib
JOSS Publication
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems
Authors
Institute for Computational Engineering & Sciences, The University of Texas at Austin
Applied Mathematics, School of Natural Sciences, University of California, Merced
Institute for Computational Engineering & Sciences, Department of Mechanical Engineering, and Department of Geological Sciences, The University of Texas at Austin
Editor
Lindsey Heagy
Tags
Infinite-dimensional inverse problems adjoint-based methods numerical optimization Bayesian inference uncertainty quantification PDE toolkitCitation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Villa"
given-names: "Umberto"
- family-names: "Petra"
given-names: "Noemi"
- family-names: "Ghattas"
given-names: "Omar"
title: "hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs"
version: 3.0.0
doi: 10.5281/zenodo.1234
date-released: 2020-02-02
url: "https://github.com/hippylib/hippylib"
references:
- type: article
authors:
- family-names: "Villa"
given-names: "Umberto"
- family-names: "Petra"
given-names: "Noemi"
- family-names: "Ghattas"
given-names: "Omar"
title: "hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference"
journal: "ACM Trans. Math. Softw."
volume: 47
issue: 2
year: 2021
doi: 10.1145/3428447
GitHub Events
Total
- Issues event: 4
- Watch event: 9
- Issue comment event: 3
- Push event: 5
- Pull request review comment event: 15
- Pull request review event: 19
- Pull request event: 5
- Fork event: 5
Last Year
- Issues event: 4
- Watch event: 9
- Issue comment event: 3
- Push event: 5
- Pull request review comment event: 15
- Pull request review event: 19
- Pull request event: 5
- Fork event: 5
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Umberto Villa | u****a@g****m | 161 |
| Umberto Villa | u****a@i****u | 15 |
| tomoleary | t****y@g****m | 7 |
| Graham Pash | g****6@g****m | 3 |
| siddhantwahal | 3****l | 3 |
| Dingcheng Luo | 4****o | 2 |
| Noemi Petra | n****a@u****u | 2 |
| Christian Boehm | c****m@e****h | 1 |
| Eldar Khattatov | e****h | 1 |
| Evan1578 | 3****8 | 1 |
| Mathew | 5****u | 1 |
| Matthias Bussonnier | b****s@g****m | 1 |
| Tucker Hartland | t****d@g****m | 1 |
| joshuawchen | j****n@u****u | 1 |
| ljlozenski | 3****i | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 38
- Total pull requests: 47
- Average time to close issues: 11 months
- Average time to close pull requests: 3 months
- Total issue authors: 20
- Total pull request authors: 15
- Average comments per issue: 2.87
- Average comments per pull request: 1.57
- Merged pull requests: 41
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 3
- Pull requests: 8
- Average time to close issues: about 9 hours
- Average time to close pull requests: 18 days
- Issue authors: 3
- Pull request authors: 4
- Average comments per issue: 2.0
- Average comments per pull request: 0.0
- Merged pull requests: 6
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- joshuawchen (12)
- uvilla (5)
- quang-ha (2)
- eldarkh (2)
- siddhantwahal (2)
- cepez (1)
- tanggo9 (1)
- arielxinsu (1)
- DavideBaroliUniLu (1)
- hphuoctruong (1)
- ldong87 (1)
- JinwooGo (1)
- lcao11 (1)
- ElisabethBrockhausQC (1)
- thartland (1)
Pull Request Authors
- uvilla (18)
- tomoleary (9)
- gtpash (7)
- dc-luo (4)
- siddhantwahal (3)
- eldarkh (3)
- mathewgaohu (2)
- Carreau (2)
- thartland (1)
- joshuawchen (1)
- dependabot[bot] (1)
- ljlozenski (1)
- bonh (1)
- boehmc (1)
- Evan1578 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 179 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 3
- Total maintainers: 2
pypi.org: hippylib
an Extensible Software Framework for Large-scale Deterministic and Bayesian Inverse Problems
- Homepage: https://hippylib.github.io/
- Documentation: https://hippylib.readthedocs.io/
- License: GNU General Public License v2 (GPLv2)
-
Latest release: 3.1.0
published about 3 years ago
Rankings
Dependencies
- m2r *
- mistune ==0.8.4
- actions/checkout v2 composite