Scalable Scientific Machine Learning Lab

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.
van Beek, J. W., Dolean, V., Moseley, B. (2026).
Computer Methods in Applied Mechanics and Engineering.
Paper    Code    Workshop

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., Robinson, D., Or, D., Hohenbrink, T. L., Minasny, B., Sadeghi, M., Arthur, E., Tuller, M., Greve, M. H., de Jonge, L. W. (2026).
Journal of Hydrology.
Paper    Imperial News Article

Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm.
Accelerated the solving, learning, and optimization of complex physical systems by reformulating finite element PDE computations as GPU-efficient tensor operations that eliminated costly loops, enabling faster, more scalable, and more accurate performance across simulation, inverse design, and physics-informed learning tasks.
Wen, S., Chi, M., Yu, T., Moseley, B., Michelis, M. Y., Ren, P., Sun, H., Mishra, S. (2026).
Proceedings of the 43rd International Conference on Machine Learning.
Paper

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