CMX Student/Postdoc Seminar

Multiphysics modeling and simulation are rapidly becoming indispensable for modern engineering and science. However, remarkable gaps still exist between state-of-the-art multiphysics simulations and reality, which can result in catastrophic loss of life or property (e.g., Europe’s Schiaparelli Mars lander crash and the record-breaking heatwave in the western United States). Core challenges for such simulations include the systems’ great range of scales in space and time, strong coupling effects among subsystems, and huge uncertainties in reality. To address these challenges, my overarching research goal is developing robust, high-fidelity, and intelligent partial differential equation (PDE) solvers, which judiciously combine theory, high-performance computing, data, and machine learning. The fundamental laws governing multiphysics systems are known; however, brute-force computing still cannot resolve all relevant scales. Data-driven models have undeniable potential for harnessing the exponentially growing volume of data. My focus on data-driven approaches includes 1) how to make the hybrid solvers, combining data-driven closure models with traditional PDE solvers, more robust; 2) how to practically leverage indirect data, which generally do not provide direct information about small-scale processes. These ideas, including physics-based neural networks and large-scale Bayesian inference, will be demonstrated through two projects: Mars landing parachute inflation simulation and climate modeling.