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ORCIDLiu Yang 0027
Person information
- affiliation: University of California Los Angeles, Department of Mathematics, CA, USA
- affiliation (former): Brown University, Division of Applied Mathematics, Providence, RI, USA
Other persons with the same name
- Liu Yang — disambiguation page
- Liu Yang 0001 — Yale University, New Haven, CT, USA (and 3 more)
- Liu Yang 0002
![[0000-0001-9915-6432]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-9915-6432")
— Guangzhou University, School of Computer Science and Education Software, China (and 1 more) - Liu Yang 0003 — Chongqing University of Posts and Telecommunications, School of Communication and Information Engineering, China
- Liu Yang 0004 — Army Engineering University of PLA, College of Communication Engineering, Nanjing, China
- Liu Yang 0005 — University of Massachusetts Amherst, College of Information and Computer Sciences, MA, USA
- Liu Yang 0006 — Beijing Jiaotong University, State Key Laboratory of Rail Traffic Control and Safety, China
- Liu Yang 0007 — Beijing Institute of Technology, School of Automation, China
- Liu Yang 0008 — Hong Kong University of Science and Technology, Department of Computer Science and Engineering, Hong Kong
- Liu Yang 0009 — Northeastern University, School of Information Science and Engineering, Shenyang, China
- Liu Yang 0010 — Tianjin University, College of Intelligence and Computing, China (and 1 more)
- Liu Yang 0011 — Guangxi University of Finance and Economics, China
- Liu Yang 0012 — China University of Mining and Technology (Beijing), College of Geoscience and Surveying Engineering, China
- Liu Yang 0013 — Nankai University, Institute of Robotics and Automatic Information System, China (and 1 more)
- Liu Yang 0014 — Norwegian University of Science and Technology, Department of Mechanical and Industrial Engineering, Trondheim, Norway
- Liu Yang 0015 — Central South University, School of Computer Science and Engineering, Changsha, China
- Liu Yang 0016 — Beijing University of Posts and Telecommunications, School of Electronic Engineering, China
- Liu Yang 0017 — Ningbo University of Technology, Research Institute of Interdisciplinary Intelligent Science, China (and 1 more)
- Liu Yang 0018 — Stony Brook University, Department of Electrical and Computer Engineering, NY, USA (and 1 more)
- Liu Yang 0019 — Northeast Forestry University, College of Computer and Control Engineering, Harbin, China (and 1 more)
- Liu Yang 0020 — Huazhong University of Science and Technology, School of Optics and Electronics Information, Wuhan, China
- Liu Yang 0021 — Zhejiang University, College of Control Science and Engineering, Hangzhou, China
- Liu Yang 0022 — Harbin Engineering University, College of Intelligent Systems Science and Engineering, China
- Liu Yang 0023 — Purdue University, School of Civil Engineering, West Lafayette, IN, USA
- Liu Yang 0024 — Nanjing University of Posts and Telecommunications, School of Internet of Things, China
- Liu Yang 0025 — Shandong University, Qingdao, China
- Liu Yang 0026 — Mayo Clinic, Department of Transplantation, Jacksonville, FL, USA
- Liu Yang 0028 — University of International Business and Economics, Beijing, China
- Liu Yang 0029 — Southeast University, School of Architecture, Nanjing, China
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2020 – today
- 2025
[j10]Liu Yang, Siting Liu, Stanley J. Osher:
Fine-tune language models as multi-modal differential equation solvers. Neural Networks 188: 107455 (2025)- [i15]Elisa Negrini, Yuxuan Liu, Liu Yang, Stanley J. Osher, Hayden Schaeffer:
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions. CoRR abs/2502.06026 (2025) - 2024
- [j9]Liu Yang, Stanley J. Osher:
PDE generalization of in-context operator networks: A study on 1D scalar nonlinear conservation laws. J. Comput. Phys. 519: 113379 (2024) - [i14]Liu Yang, Stanley J. Osher:
PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws. CoRR abs/2401.07364 (2024) - [i13]Yadi Cao, Yuxuan Liu, Liu Yang, Rose Yu, Hayden Schaeffer, Stanley J. Osher:
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction. CoRR abs/2411.16063 (2024) - 2023
- [i12]Liu Yang, Siting Liu, Tingwei Meng, Stanley J. Osher:
In-Context Operator Learning for Differential Equation Problems. CoRR abs/2304.07993 (2023) - [i11]Liu Yang, Tingwei Meng, Siting Liu, Stanley J. Osher:
Prompting In-Context Operator Learning with Sensor Data, Equations, and Natural Language. CoRR abs/2308.05061 (2023) - 2022
- [j8]Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis:
Learning functional priors and posteriors from data and physics. J. Comput. Phys. 457: 111073 (2022) - [j7]Liu Yang, Constantinos Daskalakis, George E. Karniadakis:
Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models. SIAM J. Sci. Comput. 44(1): 80- (2022) - [j6]Liu Yang, George Em Karniadakis:
Potential Flow Generator With L2 Optimal Transport Regularity for Generative Models. IEEE Trans. Neural Networks Learn. Syst. 33(2): 528-538 (2022) - 2021
- [j5]Liu Yang, Xuhui Meng, George Em Karniadakis:
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. J. Comput. Phys. 425: 109913 (2021) - [j4]Xiaoli Chen, Liu Yang, Jinqiao Duan, George Em Karniadakis:
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-Informed Neural Networks. SIAM J. Sci. Comput. 43(3): B811-B830 (2021) - [i10]Liu Yang, Tingwei Meng, George Em Karniadakis:
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates. CoRR abs/2101.06802 (2021) - [i9]Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis:
Learning Functional Priors and Posteriors from Data and Physics. CoRR abs/2106.05863 (2021) - 2020
- [j3]Liu Yang, Dongkun Zhang, George Em Karniadakis:
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations. SIAM J. Sci. Comput. 42(1): A292-A317 (2020) - [i8]Dixia Fan, Liu Yang, Michael S. Triantafyllou, George Em Karniadakis:
Reinforcement Learning for Active Flow Control in Experiments. CoRR abs/2003.03419 (2020) - [i7]Liu Yang, Xuhui Meng, George Em Karniadakis:
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data. CoRR abs/2003.06097 (2020) - [i6]Liu Yang, Constantinos Daskalakis, George Em Karniadakis:
Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations. CoRR abs/2008.01915 (2020) - [i5]Xiaoli Chen, Liu Yang, Jinqiao Duan, George Em Karniadakis:
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks. CoRR abs/2008.10653 (2020)
2010 – 2019
- 2019
- [j2]Guofei Pang, Liu Yang, George E. Karniadakis:
Neural-net-induced Gaussian process regression for function approximation and PDE solution. J. Comput. Phys. 384: 270-288 (2019) - [c1]Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston:
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC 2019: 1-11 - [i4]Liu Yang, George E. Karniadakis:
Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models. CoRR abs/1908.11462 (2019) - [i3]Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis:
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR abs/1910.13444 (2019) - 2018
- [j1]Dongkun Zhang, Liu Yang, George E. Karniadakis:
Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors. J. Comput. Phys. 374: 121-134 (2018) - [i2]Guofei Pang, Liu Yang, George E. Karniadakis:
Neural-net-induced Gaussian process regression for function approximation and PDE solution. CoRR abs/1806.11187 (2018) - [i1]Liu Yang, Dongkun Zhang, George E. Karniadakis:
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations. CoRR abs/1811.02033 (2018)
Coauthor Index
aka: George E. Karniadakis
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