This GitHub organization contains software projects related to state-of-the-art scalable adjoint-based algorithms for PDE-based deterministic and Bayesian inverse problems, optimization under uncertainty with PDE constraints, optimal design of experiments, and learning of surrogate models.
Key software projects:
- hIPPYlib: PDE-based deterministic and Bayesian inverse problems. hIPPYlib uses FEniCS for the discretization of the PDE and PETSc for parallel linear algebra
- soupy: optimization under uncertainty with PDE-constraints
- hIPPYlib2muq: An interface between hIPPYlib and muq to explore advanced Markov chain Monte Carlo (MCMC) methods.
- hIPPYflow: model reduction and learned surrogate models for PDE-based optimization and inverse problems
- cvips_labs: a gentle introduction to computational and variational inverse problems
