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.
Latest Release
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}
}