Lois sent out this announcement on hIPPYlib 3.0 Begin forwarded message:
From: "McInnes, Lois Curfman" <curf...@anl.gov<mailto: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<mailto: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<mailto:siam-cse-boun...@siam.org> on behalf of npe...@ucmerced.edu<mailto: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/ _______________________________________________ SIAM-CSE mailing list To post messages to the list please send them to: siam-...@siam.org<mailto:siam-...@siam.org> http://lists.siam.org/mailman/listinfo/siam-cse