A cGAN Ensemble-based Uncertainty-aware Surrogate Model for Offline Model-based Optimization in Industrial Control Problems
This addresses offline model-based optimization for industrial control problems, offering a practical solution for noisy data environments, though it appears incremental in its approach.
The study tackled the problem of creating reliable probabilistic models for noisy industrial data and optimizing control parameters without active feedback, introducing a cGAN ensemble-based uncertainty-aware surrogate model that outperformed competitive baselines in discrete and continuous control cases.
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. The effectiveness of the proposed method is demonstrated through extensive experiments conducted on two representative cases, namely a discrete control case and a continuous control case. The results of these experiments show that our method outperforms several competitive baselines in the field of offline model-based optimization for industrial control.