Scalable Scientific Machine Learning Lab

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

History-Matching of Imbibition Flow in Fractured Porous Media Using Physics-Informed Neural Networks (PINNs).
Demonstrated that physics-informed neural networks can efficiently and accurately model multiphase flow in fractured porous media, validated against experimental data and numerical simulations.
Abbasi, J., Moseley, B., Kurotori, T., Jagtap, A., Kovscek, A., Hiorth, A., Andersen, P. (2025).
Computer Methods in Applied Mechanics and Engineering.
Paper

Multilevel domain decomposition-based architectures for physics-informed neural networks.
Improved the performance of physics-informed neural networks by using multiple levels of domain decompositions to model different frequency scales in their outputs.
Dolean, V., Heinlein, A., Mishra, S., Moseley, B. (2024).
Computer Methods in Applied Mechanics and Engineering.
Paper    Code