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Life sciences · Preprint
arXiv · September 3, 2026
Raises a question worth testing. It does not answer one.
This is a preprint describing a computational framework that combines finite-volume solvers with neural networks to learn constitutive laws and initial conditions for population balance equations in chemical transport. No experimental validation, comparison to existing methods, or quantitative performance metrics are provided.
Preprint.
Framework integrates JAX finite volume population balance solver with learnable neural network components System is designed to discover constitutive laws and fit initial conditions from experimental data Differentiability enables process optimisation for experimental settings
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
A methodological proof-of-concept introducing a differentiable hybrid modelling framework for chemical transport processes; no experimental validation, clinical outcomes, or comparative efficacy data are presented.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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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 general-purpose differentiable hybrid modelling framework for transport processes, specifically for the case of population balance equations. Our framework integrates a JAX finite volume population balance solver with learnable neural network components which are trained to both discover constitutive laws and fit initial conditions from real experimental data, allowing us to better model real experimental transport systems. Furthermore, we use our framework for process optimisation, using its differentiability to allow us to direct optimising experimental settings for quantities of interest. This work highlights the huge potential of such differentiable hybrid modelling frameworks for learning and optimising any given chemical separation which involves mass, energy, and/or momentum transport.
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