Probabilistic neural network framework embeds linear equality constraints for dynamic chemical process modeling, showing improved accuracy, calibration, and constraint adherence on reduced data plus faster training on large data.
Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference
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abstract
Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predictions may also violate known physical knowledge. We propose a Bayesian framework to embed linear relationships across inputs and outputs into the learning process, whilst characterizing full predictive uncertainty over both the model parameters and the domain knowledge. We evaluated our method on learning the single particle battery model subject to voltage and energy balances, showing its ability to provide reduced credible intervals and constraint violations compared to standard Bayesian neural networks based on variational inference.
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cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling
Probabilistic neural network framework embeds linear equality constraints for dynamic chemical process modeling, showing improved accuracy, calibration, and constraint adherence on reduced data plus faster training on large data.