This research develops patient-specific digital twins of the heart to improve radiofrequency ablation for cardiac arrhythmias. By simulating heat transfer, tissue damage, and electrical activity, these computational models could improve treatment accuracy, reduce repeat procedures, accelerate medical device development, and advance the future of personalised cardiovascular medicine.

This research uses artificial intelligence to accelerate scientific simulations by learning patterns from traditional mathematical models. Rather than replacing physics, the AI predicts efficient starting points for complex calculations, producing accurate results much faster. The approach could dramatically speed up research in fields such as medicine, engineering, and climate science.

This research combines galaxy simulations with machine learning to study the invisible gas surrounding galaxies. By training a neural network to interpret astronomical observations, the project creates a public tool—the Circumgalactic Dictionary—that enables previously impossible measurements, advancing our understanding of galaxy evolution and the origins of stars, planets, and life.

This research develops a machine-learning and data-assimilation framework that combines idealized and operational Earth systems models into a high-resolution, physically realistic “bridging model.” Applied to the El Niño–Southern Oscillation, the approach improves climate simulation accuracy while enabling exploration of alternative climate regimes and physically consistent what-if scenarios.