Stabilization of industrial processes with time series machine learning
The paper proposes a machine learning pipeline to stabilize time series processes by replacing traditional point-wise optimization with training neural network weights. This method achieves significantly better stability than classical solvers by training an optimizer model to mimic the best control policy, leading to improved process regulation.