Computational Mechanics & Scientific Artificial Intelligence Lab (CoMSAIL) focuses on developing the next-generation computational modeling & simulation capability for various computational mechanics problems by integrating multi-resolution data, classic numerical solver, and deep neural networks based on differentiable programming, Bayesian learning/optimization, GPU computing, and uncertainty quantification.
National Science Foundation
- CAREER: Forward and Inverse Uncertainty Quantification of Cardiovascular Fluid-Structure Dynamics via Multi-fidelity Physics-Informed Bayesian Geometric Deep Learning (NSF OAC, 2021 – 2026).
- OAC Core: A Machine Learning Assisted Visual Analytics Approach for Understanding Flow Surfaces (NSF OAC, 2021-2024)
- Physics-Constrained Deep Learning for Surrogate Modeling of Dynamics of Fluids and Fluid-Structure Interaction (NSF CMMI, 2019-2022)
Office of Naval Research (ONR)
- Young Investigator Program (YIP) Award: Physics-Preserved Neural Differentiable Computing for Predictive Modeling of Rough-Wall Turbulence. (ONR YIP, 2022 – 2025)
Air Force Office of Scientific Research
- Optimizing Carbon/Carbon Composites Manufacturing by Identifying and Reducing Key Uncertainties for Hypersonic Applications (AFOSR, 2022 – 2025)
Defense Advanced Research Projects Agency (DARPA)
- Physics-Informed Learning for Multiscale Systems (DARPA, 2018 – 2020)
Lucy Family Institute for Data & Society
- Rapid Personalized Image-Based Cardiovascular Flow Modeling Using Bayesian Deep Learning (Lucy, 2022 – 2023)
ND Energy Center for Sustainable Energy
ND Environmental Change Initiative (ECI)
- Wind Energy Harvesting based on Flow-induced Vibrations and Community Acceptance (Postdoc Fellowship, 2022-2023)
AnalytiXIN
- AnalytiXIN Manufacturing Data Asset Research (AnalytiXIN, 20222-2024)
