We are a research group at University of Pennsylvania led by Prof. Nat Trask.
We develop machine learning methods grounded in the structure of physics and mathematics. By combining tools from geometric mechanics, exterior calculus, and variational modeling with modern AI architectures, we create interpretable and reliable models for complex physical systems. Our work spans simulation, model discovery, and data-driven inference across multiscale and multiphysics domains—including energy, climate, fusion, and soft matter—where traditional approaches break down. At the core of our mission is a commitment to building scientifically faithful AI that advances understanding, not just prediction.
We are looking for passionate new PhD students, Postdocs, and Master students to join the team (see openings) !
