Research Interest

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