Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data
Transport models underpin the design and optimisation of chemical processes, yet their constitutive laws are typically hand-picked, their experimental initial conditions are assumed exact, and their computational cost restricts optimisation to a handful of process variables. Here we introduce a differentiable hybrid modelling framework that embeds neural networks within a GPU-accelerated population balance equation solver to discover constitutive laws and assimilate initial conditions directly from experimental crystallisation data. Using thirteen real experiments with multi-dimensional size and shape measurements, we show that treating measured initial conditions as exact yields a physic...
| Authors | Arthur Jessop, Mohammed Alsubeihi, Ben Moseley et al. |
|---|---|
| Primary category | cs.CE |
| Published | 3 September 2026 |
| Last updated at source | 9 October 2026 |
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