Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling
Matthew Marsh, Benoit Chachuat, Antonio del Rio Chanona
Abstract
Machine learning models are increasingly used to model chemical process systems, yet they often lack principled uncertainty quantification and mechanisms to enforce physical constraints. We propose a probabilistic neural network framework that guarantees satisfaction of linear equality constraints within a given tolerance, while capturing aleatoric uncertainty. Compared to state-of-the-art methods, our formulation demonstrates improved predictive accuracy, uncertainty calibration, and adherence to constraints on reduced data. It also demonstrates competitive performance, but with significantly faster training times when evaluated on large data regimes. We evaluated this on two batch reactor case studies, enforcing mass balances.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.