arXiv Machine Learning
Sep 4

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

The paper presents a differentiable hybrid modelling framework that combines a JAX finite volume population balance solver with learnable neural network components. This approach learns constitutive laws and initial conditions directly from experimental data, improving the fidelity of transport models in chemical engineering. The framework’s differentiability also enables optimisation of experimental settings for desired process outcomes.

By Arthur Jessop, Mohammed Alsubeihi, Ben Moseley, Ashwin Kumar Rajagopalan
Hugging Face Trending Papers
Sep 3

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

The paper presents a differentiable hybrid modelling framework that combines a JAX finite volume population balance solver with neural network components to learn constitutive laws and initial conditions directly from experimental data. This approach addresses biases from hand‑picked models and the limitations of black‑box surrogates, enabling more accurate transport predictions. The framework’s differentiability also facilitates optimisation of experimental settings for desired process outcomes.

arXiv AI
6d ago

RD-JEPA: Predictive latent pretraining for few-trajectory transfer across reaction--diffusion equations

RD‑JEPA is a joint‑embedding predictive architecture designed for self‑supervised pretraining on reaction‑diffusion trajectories. The model is pretrained on five parameterized systems and then adapted to three held‑out systems that were not seen during pretraining. Using as few as one, five, or ten complete trajectories from a held‑out system, RD‑JEPA outperforms five supervised surrogate baselines, an independently trained control that removes the trajectory‑dependent predictive latent pathway, and an architecture‑matched model trained from scratch, achieving lower mean relative discrete β field error and mean absolute spatial first‑difference error across various output resolutions, forecast horizons, and adaptation trajectory choices.

By Chenhao Si, Ming Yan