Scalable Scientific Machine Learning Lab

A differentiable hybrid modeling approach for learning soil water retention mechanisms from partial knowledge and data.
Reframed how soil water retention is modelled by augmenting traditional modelling with machine learning components that learnt previously uncertain physics directly from data. Using this method, we accurately reproduced soil water retention behaviour and uncovered new pore-scale insights.
Norouzi, S., Moldrup, P., Moseley, B. et al (2026).
Journal of Hydrology.
Paper

Local Feature Filtering for Scalable and Well-Conditioned Domain-Decomposed Random Feature Methods.
Significantly accelerated the training of physics-informed neural networks by using random features and domain decomposition to turn their optimisation problem into a structured least squares problem and proposing a novel preconditioner to accelerate convergence.
Willem van Beek, J., Dolean, V., Moseley, B. (2025).
Computer Methods in Applied Mechanics and Engineering.
Paper    Code    Workshop

Challenges and advancements in modeling shock fronts with physics-informed neural networks: A review and benchmarking study.
Explores how to improve physics-informed neural networks for solving equations that involve sudden physical changes – like shock waves – highlighting their current limitations and the need for better techniques to handle complex problems accurately.
Abbasi, J., Jagtap, A., Moseley, B., Hiorth, A., Andersen, P. (2025).
Neurocomputing.
Paper    Preprint

Modern, Efficient, and Differentiable Transport Equation Models Using JAX: Applications to Population Balance Equations.
Developed a modern JAX-based solver that achieves up to 300x acceleration in population balance equation simulations, enabling faster, fully differentiable models to automate engineering processes and shorten drug development timelines.
Alsubeihi, M., Jessop, A., Moseley, B., Fonte, C.P., Rajagopalan, A.K. (2025).
Industrial & Engineering Chemistry Research.
Paper