Liu Yang

Orcid: 0000-0002-7476-9168

Affiliations:
  • University of California Los Angeles, Department of Mathematics, CA, USA
  • Brown University, Division of Applied Mathematics, Providence, RI, USA (former)


According to our database1, Liu Yang authored at least 20 papers between 2018 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

On csauthors.net:

Bibliography

2025
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions.
CoRR, February, 2025

Fine-tune language models as multi-modal differential equation solvers.
Neural Networks, 2025

2024
PDE generalization of in-context operator networks: A study on 1D scalar nonlinear conservation laws.
J. Comput. Phys., 2024

VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction.
CoRR, 2024

2023
Prompting In-Context Operator Learning with Sensor Data, Equations, and Natural Language.
CoRR, 2023

In-Context Operator Learning for Differential Equation Problems.
CoRR, 2023

2022
Potential Flow Generator With L<sub>2</sub> Optimal Transport Regularity for Generative Models.
IEEE Trans. Neural Networks Learn. Syst., 2022

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models.
SIAM J. Sci. Comput., 2022

Learning functional priors and posteriors from data and physics.
J. Comput. Phys., 2022

2021
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-Informed Neural Networks.
SIAM J. Sci. Comput., 2021

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data.
J. Comput. Phys., 2021

Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates.
CoRR, 2021

2020
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations.
SIAM J. Sci. Comput., 2020

Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations.
CoRR, 2020

Reinforcement Learning for Active Flow Control in Experiments.
CoRR, 2020

2019
Neural-net-induced Gaussian process regression for function approximation and PDE solution.
J. Comput. Phys., 2019

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs.
CoRR, 2019

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models.
CoRR, 2019

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs.
Proceedings of the Third IEEE/ACM Workshop on Deep Learning on Supercomputers, 2019

2018
Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors.
J. Comput. Phys., 2018


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