Bio
My research is focused on the integration of physical and mathematical structure into machine learning architectures, providing mathematically rigorous paths toward developing AI-driven tools. The techniques lie primarily in concepts related to exterior calculus and geometric/variational mechanics, and provide a means of extracting models that can be used in extreme physics settings when the derivation of solution of first-principles model is intractable. I have a particular interest in the relationship between graph neural networks and traditional finite element discretizations of continuum models. I use these techniques to construct probabilistic digital twins and perform autonomous scientific discovery, and have worked in a number of multiscale/multiphysics application areas including: combustion, energy storage, climate simulation, fusion power, multiphase flows, fracture, and soft matter.