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A cGAN Ensemble-based Uncertainty-aware Surrogate Model for Offline Model-based Optimization in Industrial Control Problems
March 26, 2024, 4:44 a.m. | Cheng Feng
cs.LG updates on arXiv.org arxiv.org
Abstract: This study focuses on two important problems related to applying offline model-based optimization to real-world industrial control problems. The first problem is how to create a reliable probabilistic model that accurately captures the dynamics present in noisy industrial data. The second problem is how to reliably optimize control parameters without actively collecting feedback from industrial systems. Specifically, we introduce a novel cGAN ensemble-based uncertainty-aware surrogate model for reliable offline model-based optimization in industrial control problems. …
abstract arxiv control cs.ai cs.lg cs.sy data dynamics eess.sy ensemble industrial industrial control offline optimization probabilistic model study type uncertainty world
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