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Liu Yang
Liu Yang
Department of Mathematics, University of California, Los Angeles
在 math.ucla.edu 的电子邮件经过验证 - 首页
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引用次数
引用次数
年份
Physics-informed machine learning
GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang
Nature Reviews Physics 3 (6), 422-440, 2021
38512021
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
L Yang, X Meng, GE Karniadakis
Journal of Computational Physics 425, 109913, 2021
7222021
Physics-informed generative adversarial networks for stochastic differential equations
L Yang, D Zhang, GE Karniadakis
SIAM Journal on Scientific Computing 42 (1), A292-A317, 2020
3752020
Reinforcement learning for bluff body active flow control in experiments and simulations
D Fan, L Yang, Z Wang, MS Triantafyllou, GE Karniadakis
Proceedings of the National Academy of Sciences 117 (42), 26091-26098, 2020
2082020
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks
X Chen, L Yang, J Duan, GE Karniadakis
SIAM Journal on Scientific Computing 43 (3), B811-B830, 2021
972021
Neural-net-induced Gaussian process regression for function approximation and PDE solution
G Pang, L Yang, GE Karniadakis
Journal of Computational Physics 384, 270-288, 2019
892019
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
L Yang, S Treichler, T Kurth, K Fischer, D Barajas-Solano, J Romero, ...
2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), 1-11, 2019
542019
Learning functional priors and posteriors from data and physics
X Meng, L Yang, Z Mao, J del Águila Ferrandis, GE Karniadakis
Journal of Computational Physics 457, 111073, 2022
462022
Potential Flow Generator With L2 Optimal Transport Regularity for Generative Models
L Yang, GE Karniadakis
IEEE Transactions on Neural Networks and Learning Systems 33 (2), 528-538, 2020
442020
Generative ensemble regression: Learning particle dynamics from observations of ensembles with physics-informed deep generative models
L Yang, C Daskalakis, GE Karniadakis
SIAM Journal on Scientific Computing 44 (1), B80-B99, 2022
31*2022
In-context operator learning with data prompts for differential equation problems
L Yang, S Liu, T Meng, SJ Osher
Proceedings of the National Academy of Sciences 120 (39), e2310142120, 2023
24*2023
Fine-Tune Language Models as Multi-Modal Differential Equation Solvers
L Yang, T Meng, S Liu, SJ Osher
arXiv preprint arXiv:2308.05061, 2023
7*2023
Pde generalization of in-context operator networks: A study on 1d scalar nonlinear conservation laws
L Yang, SJ Osher
arXiv preprint arXiv:2401.07364, 2024
42024
Deep reinforcement learning for bluff body active flow control in experiments and simulations
D Fan, L Yang, Z Wang, M Triantafyllou, G Karniadakis
APS Division of Fluid Dynamics Meeting Abstracts, R01. 010, 2020
22020
Measure-conditional discriminator with stationary optimum for GANs and statistical distance surrogates
L Yang, T Meng, GE Karniadakis
arXiv preprint arXiv:2101.06802, 2021
12021
Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors
D Zhang, L Yang, GE Karniadakis
Journal of Computational Physics 374, 121-134, 2018
12018
Yin, Junqi 84 Zhang, Zhao 45, 69
C Adams, AA Awan, D Barajas-Solano, JK Bassett, D Bhowmik, T Bicer, ...
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