Contact

• Email: liuyang(at)math(dot)ucla(dot)edu
• Office: 520 Portola Plaza, MS 7620F, Los Angeles, CA 90095
• Links: Google Scholar, GitHub

Education

• Ph.D. in Applied Mathematics
  Brown University, May 2021
• Sc.M. in Applied Mathematics
  Brown University, May 2018
• B.E. in Engineering Mechanics
  Tsinghua University, July 2016

Work

• Assistant Professor
  Department of Mathematics, National University of Singapore, July 2024 -
• Assistant Adjunct Professor
  Department of Mathematics, UCLA, July 2022 - June 2024
• Automonous Driving Engineer
  WeRide.ai, June 2021 - June 2022
  Working on motion planning algorithms that ensure safety in uncertain environments, with solid mathematical foundations and great performance in actual products.

Brief Biography

I am currently an Assistant Adjunct Professor in the Department of Mathematics at UCLA, working with Prof. Stanley Osher. I obtained my Ph.D. in Applied Mathematics from Brown University, advised by Prof. George Em Karniadakis, with a dissertation on Generative Adversarial Networks for Physics-Informed Learning. My research interests are in artificial intelligence and scientific computing.

Curriculum Vitae

Check out PDF file.

Awards, Honors & Scholarships

• Presidential Young Professorship, National University of Singapore, July 2024

• David Gottlieb Memorial Award, Brown University, March 2021

• Outstanding Graduate Honor (Top 10%), Tsinghua University, July 2016

• Scholarship for Academic Excellence, Tsinghua University, November 2015

• Scholarship for Academic Excellence, Tsinghua University, November 2013

• Tsien’s Elite Class in Mechanics, Tsinghua University, 2012-2016

Publications

🔗 Google Scholar Profile (3000+ citations)

* indicates equal contribution.

• “PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws” arXiv:2401.07364, 2024.
  Liu Yang, and Stanley J. Osher.

• “Fine-Tune Language Models as Multi-Modal Differential Equation Solvers” arXiv:2308.05061, 2023.
  Liu Yang, Siting Liu, and Stanley J. Osher.

• “In-Context Operator Learning With Data Prompts for Differential Equation Problems” Proceedings of the National Academy of Sciences, 2023.
  Liu Yang, Siting Liu, Tingwei Meng, and Stanley J. Osher.

• “Learning Functional Priors and Posteriors From Data and Physics” Journal of Computational Physics, 2022.
  *Xuhui Meng, *Liu Yang, Zhiping Mao, José del Águila Ferrandis, and George Em Karniadakis.

• “Generative Ensemble Regression: Learning Particle Dynamics From Observations of Ensembles With Physics-Informed Deep Generative Models” SIAM Journal on Scientific Computing, 2022.
  Liu Yang, Constantinos Daskalakis, and George E. Karniadakis.

• “Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates” arXiv:2101.06802, 2021.
  Liu Yang, Tingwei Meng, and George E. Karniadakis.

• “Physics-Informed Machine Learning” Nature Reviews Physics, 2021.
  George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. (alphabetical order)

• “B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems With Noisy Data” Journal of Computational Physics, 2021.
  *Liu Yang, *Xuhui Meng, and George Em Karniadakis.

• “Solving Inverse Stochastic Problems From Discrete Particle Observations Using the Fokker–Planck Equation and Physics-Informed Neural Networks” SIAM Journal on Scientific Computing, 2021.
  Xiaoli Chen, Liu Yang, Jinqiao Duan, and George Em Karniadakis.

• “Reinforcement Learning for Bluff Body Active Flow Control in Experiments and Simulations” Proceedings of the National Academy of Sciences, 2020.
  *Dixia Fan, *Liu Yang, *Zhicheng Wang, Michael S. Triantafyllou, and George Em Karniadakis.

• “Potential Flow Generator With $L_2$ Optimal Transport Regularity for Generative Models” IEEE Transactions on Neural Networks and Learning Systems, 2020.
  Liu Yang, and George Em Karniadakis.

• “Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations” SIAM Journal on Scientific Computing, 2020.
  Liu Yang, Dongkun Zhang, and George Em Karniadakis.

• “Neural-Net-Induced Gaussian Process Regression for Function Approximation and PDE Solution” Journal of Computational Physics, 2019.
  Guofei Pang, Liu Yang, and George Em Karniadakis.

• “Bi-Directional Coupling Between a PDE-Domain and an Adjacent Data-Domain Equipped With Multi-Fidelity Sensors” Journal of Computational Physics, 2018.
  Dongkun Zhang, Liu Yang, and George Em Karniadakis.

Teaching

• Instructor
  Program in Computing 10B: Intermediate Programming (C++), UCLA, Winter 2024

• Instructor
  Program in Computing 16A: Python with Application I, UCLA, Winter, Spring & Fall 2023

• Instructor
  Program in Computing 10A: Introduction to Programming (C++), UCLA, Fall 2022

• Teaching Assistant
  Fast Learning Algorithms for Numerical Computation and Data Analysis
  The Institute for Computational and Experimental Research in Mathematics (ICERM), Summer 2020

• Teaching Assistant
  Operations Research: Deterministic Models, Brown University, Spring 2020

• Teaching Assistant
  Statistical Inference, Brown University, Fall 2019

Selected Talks & Conferences

• The American Mathematical Society’s Mathematics Research Communities Program, May 28–June 3, 2023

• Generative Ensemble-Regression: Learn Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models, SIAM Conference on Applications of Dynamical Systems, May 14-18, 2023

• Generative Ensemble-Regression: Learn Particle Dynamics from Observations of Ensembles with Physics-Informed Deep Generative Models, U.S. National Congress on Computational Mechanics, July 25-29, 2021

• Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations, SIAM Conference on Computational Science and Engineering, March 1-5, 2021

• Learn Nonlocal Flocking Dynamics by Generative Ensemble Regression, One Nonlocal World, Opening Event, January 22-23, 2021

• Physics-Informed Neural Networks (PINNs), Physics-Informed GANs and Bayesian PINNs, IBM Corporation, July 23, 2020

• Physics-Informed GANs for Stochastic Differential Equations, SIAM Conference on Computational Science and Engineering, February 25-March 1, 2019