A perspective on physics-constrained machine learning for chemical engineering that summarizes approaches, applications, and open challenges without introducing a new method.
Development of steady-state and dynamic mass and energy constrained neural networks for distributed chemical systems using noisy transient data
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Physics-Constrained Machine Learning for Chemical Engineering
A perspective on physics-constrained machine learning for chemical engineering that summarizes approaches, applications, and open challenges without introducing a new method.