Scalable Scientific Machine Learning Lab
@ Imperial College London
We accelerate science by building robust, scalable scientific machine learning algorithms.
Latest News 🚀
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Dr. Ben Moseley joins editorial board of new ACM journal on AI for science
We’re excited to share that Dr. Ben Moseley has joined the editorial board of the new ACM Transactions on AI for Science (TAIS) as an… Read more →
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New SciML lab at Imperial College London!
We are very excited to announce the formation of our new research group, the Scalable Scientific Machine Learning Lab. The group is led by Dr.… Read more →
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Dr. Ben Moseley joins Imperial
Dr. Ben Moseley will be joining Imperial College London as a Lecturer in AI at the Department of Earth Science and Engineering. He will hold… Read more →
Project Highlights 🌍
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Multi-scale simulation with physics-informed neural networks
Overview Physics-informed neural networks (PINNs) have emerged as a promising tool for solving differential equations. They have been applied to many scientific problems and a… Read more →
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SciML-enhanced planetary exploration: advancing lunar and martian imaging
Overview Scientists and engineers leading missions like NASA’s Artemis program and future Mars expeditions, along with planetary researchers studying our solar system’s evolution, rely heavily… Read more →
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Machine learning with geodesic flows
Overview Many physical, biological and engineering systems evolve over time according to geometric laws, for example planets follow elliptical orbits shaped by gravity, and fluids… Read more →
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Efficient and differentiable population balance modelling with JAX
Overview Population balance equations (PBEs) are used to model the evolution of populations of particles over time, such as in crystallisation processes, chemical reactors, and… Read more →
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Extending quantum theories with AI
Overview Quantum theory is incredibly powerful for predicting the probabilities of what we’ll see in experiments, but it cannot tell us the certain outcome of… Read more →
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Weather and climate modelling with neural differential equations
Overview This is a new direction for the lab – more to come! Team & collaborators Read more →
Latest Publications 📊
Modern, Efficient, and Differentiable Transport Equation Models Using JAX: Applications to Population Balance Equations.
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
The Team 🎯
Collaborators ✨
Interested in collaborating with us?