[petsc-dev] Fwd: [SIAM-CSE] Introducing hIPPYlib, a python-based inverse problems solver library

[Smith, Barry F.]

  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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