hIPPYlib - Inverse Problem PYthon library

hIPPYlib 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.

Features

• Friendly, compact, near-mathematical FEniCS notation to express the PDE and likelihood in weak form
• Automatic generation of efficient code for the discretization of weak forms using FEniCS
• Symbolic differentiation of weak forms to generate derivatives and adjoint information
• Globalized Inexact Newton-CG method to solve the inverse problem
• Low rank representation of the posterior covariace using randomized algorithms

See also our tutorial and list of related publications.

Announcements

• Open postdoc position in Professor Noemi Petra's group. Apply here.

• June 2018: Gene Golub SIAM Summer School in Breckenridge, Colorado. Early announement flyer; more details coming soon.

Latest Release

• Development version
• Download hippylib-1.3.0.tar.gz
• Previous releases

Contact

Developed by the hIPPYlib team at UT Austin and UC Merced.

To ask question and find answers see here.

Please cite with

@article{VillaPetraGhattas2016,
title = "{hIPPYlib: an Extensible Software Framework for Large-scale Deterministic and Linearized Bayesian Inversion}",
author = {Villa, U. and Petra, N. and Ghattas, O.},
year = {2016},
url = {http://hippylib.github.io}
}