  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.
