Composite Bayesian Optimization in function spaces using NEON—Neural Epistemic Operator Networks

Composite Bayesian Optimization in function spaces using 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 uncert...

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Bibliographic Details
Main Authors: Leonardo Ferreira Guilhoto, Paris Perdikaris
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Deep learning
Autonomous experimentation
Uncertainty quantification
Online Access:https://doi.org/10.1038/s41598-024-79621-7
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https://doi.org/10.1038/s41598-024-79621-7

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