no code implementations • 8 May 2024 • Dhruv V. Patel, Jonghyun Lee, Matthew W. Farthing, Peter K. Kitanidis, Eric F. Darve
In this multi-fidelity algorithm, the acceptance probability is computed in the first stage via a standard HMC proposal using an inexpensive differentiable surrogate model, and if the proposal is accepted, the posterior is evaluated in the second stage using the high-fidelity (HF) numerical solver.
no code implementations • 15 Feb 2022 • Deep Ray, Harisankar Ramaswamy, Dhruv V. Patel, Assad A. Oberai
In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems.
1 code implementation • 27 Mar 2020 • Dhruv V. Patel, Assad A. Oberai
Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model.
no code implementations • 25 Sep 2019 • Dhruv V. Patel, Assad A. Oberai
Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Dhruv V. Patel, Assad A. Oberai
Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a mathematical model.