hIPPYlib

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

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References in zbMATH (referenced in 22 articles , 1 standard article )

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  1. Attia, Ahmed; Leyffer, Sven; Munson, Todd S.: Stochastic learning approach for binary optimization: application to Bayesian optimal design of experiments (2022)
  2. Ba, Yuming; de Wiljes, Jana; Oliver, Dean S.; Reich, Sebastian: Randomized maximum likelihood based posterior sampling (2022)
  3. Cao, Lianghao; Ghattas, Omar; Oden, J. Tinsley: A globally convergent modified Newton method for the direct minimization of the Ohta-Kawasaki energy with application to the directed self-assembly of diblock copolymers (2022)
  4. Kaltenbacher, Barbara; Schlintl, Anna: Fractional time stepping and adjoint based gradient computation in an inverse problem for a fractionally damped wave equation (2022)
  5. Nicholson, Ruanui; Niskanen, Matti: Joint estimation of Robin coefficient and domain boundary for the Poisson problem (2022)
  6. Povala, Jan; Kazlauskaite, Ieva; Febrianto, Eky; Cirak, Fehmi; Girolami, Mark: Variational Bayesian approximation of inverse problems using sparse precision matrices (2022)
  7. Chen, Peng; Ghattas, Omar: Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters (2021)
  8. Farrell, Patrick E.; Kirby, Robert C.; Marchena-Menéndez, Jorge: Irksome: automating Runge-Kutta time-stepping for finite element methods (2021)
  9. Givoli, Dan: A tutorial on the adjoint method for inverse problems (2021)
  10. Haan, Sebastian: GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences (2021) not zbMATH
  11. Villa, Umberto; Petra, Noemi; Ghattas, Omar: hIPPYlib. An extensible software framework for large-scale inverse problems governed by PDEs. I: Deterministic inversion and linearized Bayesian inference (2021)
  12. 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)
  13. 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)
  14. Jha, Prashant K.; Cao, Lianghao; Oden, J. Tinsley: Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models (2020)
  15. Koval, Karina; Alexanderian, Alen; Stadler, Georg: Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs (2020)
  16. Reuber, Georg S.; Simons, Frederik J.: Multi-physics adjoint modeling of Earth structure: combining gravimetric, seismic, and geodynamic inversions (2020)
  17. Vuchkov, Radoslav G.; Petra, Cosmin G.; Petra, Noémi: On the derivation of quasi-Newton formulas for optimization in function spaces (2020)
  18. Chen, Peng; Villa, Umberto; Ghattas, Omar: Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty (2019)
  19. Crestel, Benjamin; Stadler, Georg; Ghattas, Omar: A comparative study of structural similarity and regularization for joint inverse problems governed by PDEs (2019)
  20. Lan, Shiwei: Adaptive dimension reduction to accelerate infinite-dimensional geometric Markov chain Monte Carlo (2019)

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