Inverse problems for complex models

a.k.a. Bayesian calibration, model uncertainty,

Inverse problems where the model is more or less a black box.

Brehmer, Johann, Gilles Louppe, Juan Pavez, and Kyle Cranmer. 2020. “Mining Gold from Implicit Models to Improve Likelihood-Free Inference.” Proceedings of the National Academy of Sciences 117 (10): 5242–9.

Cranmer, Kyle, Johann Brehmer, and Gilles Louppe. 2020. “The Frontier of Simulation-Based Inference.” Proceedings of the National Academy of Sciences, May.

Grigorievskiy, Alexander, Neil Lawrence, and Simo Särkkä. 2017. “Parallelizable Sparse Inverse Formulation Gaussian Processes (SpInGP).” In.

Plumlee, Matthew. 2017. “Bayesian Calibration of Inexact Computer Models.” Journal of the American Statistical Association 112 (519): 1274–85.

Tait, Daniel J., and Theodoros Damoulas. 2020. “Variational Autoencoding of PDE Inverse Problems.” June 28, 2020.

Welter, David E., Jeremy T. White, Randall J. Hunt, and John E. Doherty. 2015. “Approaches in Highly Parameterized Inversion—PEST++ Version 3, a Parameter ESTimation and Uncertainty Analysis Software Suite Optimized for Large Environmental Models.” USGS Numbered Series 7-C12. Techniques and Methods. Reston, VA: U.S. Geological Survey.

White, Jeremy T., Michael N. Fienen, and John E. Doherty. 2016a. pyEMU: A Python Framework for Environmental Model Uncertainty Analysis Version .01. U.S. Geological Survey.

———. 2016b. “A Python Framework for Environmental Model Uncertainty Analysis.” Environmental Modelling & Software 85 (November): 217–28.

Zhang, Dongkun, Lu Lu, Ling Guo, and George Em Karniadakis. 2019. “Quantifying Total Uncertainty in Physics-Informed Neural Networks for Solving Forward and Inverse Stochastic Problems.” Journal of Computational Physics 397 (November): 108850.