Variational Bayesian surrogate modelling with application to robust design optimisation

arXiv:2404.14857v1 Announce Type: cross
Abstract: Surrogate models provide a quick-to-evaluate approximation to complex computational models and are essential for multi-query problems like design optimisation. The inputs of current computational models are usually high-dimensional and uncertain. We consider Bayesian inference for constructing statistical surrogates with input uncertainties and intrinsic dimensionality reduction. The surrogates are trained by fitting to data from prevalent deterministic computational models. The assumed prior probability density of the surrogate is a Gaussian process. We determine the respective posterior …

abstract application approximation arxiv bayesian bayesian inference computational cs.na current design dimensionality inference inputs intrinsic math.na modelling optimisation query robust stat.ap statistical stat.ml type uncertain