Yes. I had a discussion with Noemi about using it with PETSc. The big
requirement is formulating the discretization
in FEniCS. I think it might be possible to peel back one layer of interface
and use it directly. Once I find the right
student, I will have them investigate. There are some very nice pieces in
there.
Thanks,
Matt
On Wed, Feb 5, 2020 at 7:00 PM Smith, Barry F. via petsc-dev <
petsc-dev@mcs.anl.gov> wrote:
>
> Lois sent out this announcement on hIPPYlib 3.0
>
> Begin forwarded message:
>
> *From: *"McInnes, Lois Curfman" <curf...@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." <bsm...@mcs.anl.gov>
>
> Have you seen this?
>
> On 2/4/20, 9:49 AM, "SIAM-CSE on behalf of Noemi Petra" <
> siam-cse-boun...@siam.org on behalf of npe...@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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>
>
>
>
--
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener
https://www.cse.buffalo.edu/~knepley/ <http://www.cse.buffalo.edu/~knepley/>
