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This is a Quantitative Study study.

Learning hydrocracking reaction dynamics via neural ODEs: A data‐driven, gradient‐interpretable lumped modelling framework

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This study reveals how neural ordinary differential equations improve predictive accuracy and interpretability in hydrocracking kinetics.

Neural ordinary differential equations effectively model hydrocracking reaction dynamics, enhancing predictive capabilities.
Model A and Model B showed RMSE values below 0.5 wt.% during training but dropped in high-temperature scenarios.
Analysis of model gradients revealed insights into reaction pathways and temperature sensitivities, validating kinetic principles.
The study highlights the potential for symbolic regression techniques to further refine hydrocracking models.

Ta et al. (2025) studied this question.

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