My research interest includes deep learning and reinforcement learning, optimal control, applications of deep learning algorithms on scientific computing problems such as high dimensional PDEs, mean field control and games.
11. Simulating Fokker-Planck equations via mean field control of score-based normalizing flows [arXiv]
10. Variational conditional normalizing flows for computing second-order mean field control problems [arXiv]
9. Score-based neural ordinary differential equations for computing mean field control problems [arXiv]
8. Solving Time-Continuous Stochastic Optimal Control Problems: Algorithm Design and Convergence Analysis of Actor-Critic Flow [arXiv]
7. A deep learning algorithm for computing mean field control problems via forward-backward score dynamics [url][arXiv]
6. Deep Learning Method for Partial Differential Equations and Optimal Problems [url]
5. A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee [url][arXiv]
4. A Neural Network Warm-Start Approach for the Inverse Acoustic Obstacle Scattering Problem [url][arXiv]
3. Single Time-scale Actor-critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees [url][arXiv]
2. Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks [url] [arXiv]
1. Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach [url] [arXiv]