Machine/Deep Learning
Scientific machine learning, Data- and Physics-driven machine learning techniques.
Probabilistic machine learning, Neural network architectures (Graph NN, Spiking NN, and Neural ODEs).
Physics-based deep generative models for uncertainty quantification and propagation.
Quantum computing and quantum machine learning algorithms.
LLMs using Transformers and Autonomous Vehicles.
Foundational Models for PDEs
Deep Reinforcement Learning (DRL)
Multi-Agent Reinforcement Learning (MARL)
Deep Reinforcement Learning for Fluid Flow Control
Safe and Explainable Reinforcement Learning
Offline and Meta Reinforcement Learning
DRL for Scientific Discovery and Optimization
Scientific Computation
Inverse Problems
Multi-fidelity data and models.
Domain Decomposition Methods
Computational Continuum Mechanics (High-speed flows, Acoustics, and Nonlinear Elasticity).
Multi-scale/multi-physics simulations.
Spectral/Finite Element methods, WENO, and Discontinuous Galerkin schemes.
Fractional/Non-local PDEs