Christopher E. Miles

Bayesian bilevel operator learning with low-rank adaptation for efficient uncertainty quantification of PDE inverse problems

A Bilevel Local Operator Learning framework for Bayesian inference in PDEs (B-BiLO), which enables efficient gradient-based sampling without synthetic data or adjoint equations and avoids sampling in high-dimensional weight space, as in Bayesian neural networks, by optimizing weights deterministically.