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.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Ambartsumyan, Ilona; Boukaram, Wajih; Bui-Thanh, Tan; Ghattas, Omar; Keyes, David; Stadler, Georg; Turkiyyah, George; Zampini, Stefano: Hierarchical matrix approximations of hessians arising in inverse problems governed by PDEs (2020)
- Constantinescu, Emil M.; Petra, Noémi; Bessac, Julie; Petra, Cosmin G.: Statistical treatment of inverse problems constrained by differential equations-based models with stochastic terms (2020)
- Koval, Karina; Alexanderian, Alen; Stadler, Georg: Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs (2020)
- Vuchkov, Radoslav G.; Petra, Cosmin G.; Petra, Noémi: On the derivation of quasi-Newton formulas for optimization in function spaces (2020)
- Crestel, Benjamin; Stadler, Georg; Ghattas, Omar: A comparative study of structural similarity and regularization for joint inverse problems governed by PDEs (2019)
- Attia, Ahmed; Alexanderian, Alen; Saibaba, Arvind K.: Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems (2018)
- Chen, Peng; Villa, Umberto; Ghattas, Omar: Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems (2017)