Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models

Paper recorded by Signals 4 on 2026-09-15 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16

Category: cs.LG · 机器学习 · first seen 2026-09-16

Abstract

Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address these challenges, we present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framework designed for high-dimensional, expensive black-box functions. The approach integrates Global Sens

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