Adaptive operator learning for infinite-dimensional Bayesian inverse problems

arXiv:2310.17844v2 Announce Type: replace-cross
Abstract: The fundamental computational issues in Bayesian inverse problems (BIP) governed by partial differential equations (PDEs) stem from the requirement of repeated forward model evaluations. A popular strategy to reduce such costs is to replace expensive model simulations with computationally efficient approximations using operator learning, motivated by recent progress in deep learning. However, using the approximated model directly may introduce a modeling error, exacerbating the already ill-posedness of inverse problems. Thus, balancing between accuracy and efficiency …

abstract arxiv bayesian computational costs cs.na differential math.na popular reduce simulations stat.co stat.ml stem strategy type