Category: News

Our latest research has been featured in Imperial News. In collaboration with colleagues at Aarhus University, we developed a physics-informed AI framework that challenges a long-standing assumption about how water is retained in soil. By combining machine learning with physical principles, the study reveals previously hidden processes governing soil water retention, with potential implications for…

One of our PhD students, Ardan Suphi, has joined this year’s Lunar Frontier Development Lab team, where he will be helping to improve the performance of their foundation model for the Moon. Congratulations Ardan!

We are very excited to welcome Sean De Marco to the lab as a PhD student! Sean’s research will focus on improving the ability of physics-informed neural networks to solve large, multi-scale problems by combining them with numerical methods, extending our lab’s research in this area. Before joining the lab, Sean completed a B.Eng. in…

Our group’s research has been featured in Imperial Magazine (Issue 59) as part of the article “The science machines come of age,” which highlights how AI is transforming modern scientific discovery. The piece showcases our work on SciML-enhanced planetary imaging as an example of how AI is enhancing research processes, accelerating insights, and contributing to…

We are excited to announce we have 3 workshop papers accepted at the Differentiable Systems and Scientific Machine Learning workshop at EurIPS on December 6, 2025 in Copenhagen! Here are the papers: Hybrid Learning of Transport Equations with Differentiable Neural Solvers from Experimental Data, Arthur Jessop, Mohammed Alsubeihi, Ashwin Kumar Rajagopalan, Ben Moseley Learning Soil…

Dr. Ben Moseley taught students how to design scalable physics-informed neural networks at Centrum Wiskunde & Informatica in Amsterdam during their Autumn School on Scientific Machine Learning and Numerical Methods. The full workshop recording is now on YouTube, and the slides and practical exercises are on GitHub. Here’s what he covered:Session 1: Introduction to scientific…

We are very excited to welcome Davide Staub to the lab as a PhD student! Davide brings a strong interdisciplinary background at the interface of machine learning, physics, and data-driven scientific discovery. His research will focus on developing differentiable tools to reconstruct the three-dimensional structure of exoplanet atmospheres from James Webb Space Telescope observations, advancing…

Several PhD opportunities are available in our Scalable Scientific Machine Learning Lab at Imperial College London. These projects are eligible for Imperial PhD scholarships (open to both home and overseas students, with application deadlines between Nov–Jan). Current topics include: 🔬 Multi-scale simulation with physics-informed neural networks🧠 Brain ultrasound imaging with diffusion-guided full-waveform inversion🌍 Learning fast…

One of our PhD students, Ardan Suphi, will be visiting the University of Bern to collaborate on improving multispectral imaging of Mars using SciML. He will be working alongside the Planetary Imaging Group to tailor his research of predictive models to support practical applications such as research of dynamic surface processes on Mars. This collaboration…

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 Associate Editor. With the rapid growth of AI for science, TAIS aims to provide a venue for cross-disciplinary, rigorous, and trustworthy research. One of the key challenges in this field…