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Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Reliable transport models are essential when modelling and optimising many chemical engineering processes, yet, most models assume hand-picked constitutive laws which may not reflect reality, and often assume initial conditions are known exactly. Both restrictions can significantly bias model predictions and lead to systematic error when used in predictive and control settings. Black-box neural surrogate alternatives for modelling can better match real example data, but are confined to the task they were trained on and cannot be interrogated for physical consistency. Here we introduce a genera

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