Xinliang Liu

About Xinliang Liu

Xinliang Liu

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

I am currently a postdoc in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received my Ph.D. degree in Computational Mathematics under the supervision of Prof. Lei Zhang at Shanghai Jiao Tong University in Shanghai, China in 2021. Research Interest AI for PDEs. Numerical homogenization. Neural operator Graph neural network News "MgNO: Efficient Parameterization of Linear Operators via Multigrid" joint with Juncai He and Jinchao Xu has been accepted in ICLR 2024. "ACMP: Allen-Cahn Message Passing

Events

Presented Events

Apr 14 - Apr 20, 2024

Neural Operators: Theory, Architecture, and Applications for PDEs

Xinliang Liu, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

Apr 16, 16:00 - 17:00

B1 L3 R3119

Neural Operator Multigrid

Abstract Neural operator methods provide a novel approach for solving or learning the complex mappings from parameters to solutions arising from intricate physical systems. In this talk, I will cover the foundational aspects of neural operators, encompassing both theoretical frameworks and algorithmic developments, including some well-known neural operator architectures. Additionally, I will share our recent work on applying the neural operator method to multiscale partial differential equations (PDEs). To tackle the challenges of multiscale PDEs, we have developed a neural operator with a