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., Europeâ€™s Schiaparelli Mars lander crash and the record-breaking heatwave in the western United States). Core challenges for such simulations include the systemsâ€™ 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.