[petsc-dev] Fwd: [SIAM-CSE] Introducing hIPPYlib, a python-based inverse problems solver library
Smith, Barry F.
bsmith at mcs.anl.gov
Wed Feb 5 18:00:32 CST 2020
Lois sent out this announcement on hIPPYlib 3.0
Begin forwarded message:
From: "McInnes, Lois Curfman" <curfman at anl.gov<mailto:curfman at anl.gov>>
Subject: FW: [SIAM-CSE] Introducing hIPPYlib, a python-based inverse problems solver library
Date: February 4, 2020 at 8:52:46 AM CST
To: "Smith, Barry F." <bsmith at mcs.anl.gov<mailto:bsmith at mcs.anl.gov>>
Have you seen this?
On 2/4/20, 9:49 AM, "SIAM-CSE on behalf of Noemi Petra" <siam-cse-bounces at siam.org<mailto:siam-cse-bounces at siam.org> on behalf of npetra at ucmerced.edu<mailto:npetra at ucmerced.edu>> wrote:
We are pleased to announce the availability of hIPPYlib, an extensible
software framework for solving large-scale deterministic and Bayesian
inverse problems governed by partial differential equations (PDEs)
with (possibly) infinite-dimensional parameter fields. The development
of this project is being supported by the National Science Foundation.
The current version of hIPPYlib is 3.0 and can be downloaded from:
https://hippylib.github.io
This computational tool implements state-of-the-art scalable
adjoint-based algorithms for PDE-based deterministic and Bayesian
inverse problems. It builds on FEniCS for the discretization of the
PDE and on PETSc for scalable and efficient linear algebra operations
and solvers.
A few features worth highlighting include:
- Friendly, compact, near-mathematical FEniCS notation to express,
differentiate, and discretize the PDE forward model and likelihood
function
- Large-scale optimization algorithms, such as globalized inexact
Newton-CG method, to solve the inverse problem
- Randomized algorithms for trace estimation, eigenvalues and singular
values decomposition
- Scalable sampling of Gaussian random fields
- Linearized Bayesian inversion with low-rank based representation of
the posterior covariance
- Hessian-informed MCMC algorithms to explore the posterior
distribution
- Forward propagation of uncertainty capabilities using Monte Carlo
and Taylor expansion control variates
For more details, please check out the manuscript:
http://arxiv.org/abs/1909.03948
For additional resources and tutorials please check out the teaching
material from the 2018 Gene Golub SIAM Summer School on ``Inverse
Problems: Systematic Integration of Data with Models under
Uncertainty" available at http://g2s3.com.
Umberto Villa, Noemi Petra and Omar Ghattas
--
Noemi Petra, PhD
Assistant Professor of Applied Mathematics
SIAM Student Chapter Faculty Advisor
University of California, Merced
http://faculty.ucmerced.edu/npetra/
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