Composite bayesian optimization in function spaces ising NEON-Neural Epistemic Operator Networks.

ABSTRACT: Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce Neon (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders of magnitude less trainable parameters than deep ensembles of comparable performance. We showcase the utility of this method for sequential decision-making by examining the problem of composite Bayesian Optimization (BO), where we aim to optimize a function f=g∘h , where h:X→C(Y,Rds) is an unknown map which outputs elements of a function space, and g:C(Y,Rds)→R is a known and cheap-to-compute functional. By comparing our approach to ot

SUBMITTER: Guilhoto LF 

PROVIDER: S-EPMC11589850 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature