Liu Yang - RankMe

27 papers Journal 26Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction. Trans. Mach. Learn. Res. Yadi Cao, Yuxuan Liu, Liu Yang, Rose Yu, Hayden Schaeffer, Stanley J. Osher
2025	J	`jnl`	A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions. CoRR Elisa Negrini, Yuxuan Liu, Liu Yang, Stanley J. Osher, Hayden Schaeffer
2025	J	`jnl`	Fine-tune language models as multi-modal differential equation solvers. Neural Networks Liu Yang, Siting Liu, Stanley J. Osher
2024	J	`jnl`	PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws. CoRR Liu Yang, Stanley J. Osher
2024	J	`jnl`	PDE generalization of in-context operator networks: A study on 1D scalar nonlinear conservation laws. J. Comput. Phys. Liu Yang, Stanley J. Osher
2024	J	`jnl`	VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction. CoRR Yadi Cao, Yuxuan Liu, Liu Yang, Rose Yu, Hayden Schaeffer, Stanley J. Osher
2023	J	`jnl`	In-Context Operator Learning for Differential Equation Problems. CoRR Liu Yang, Siting Liu, Tingwei Meng, Stanley J. Osher
2023	J	`jnl`	Prompting In-Context Operator Learning with Sensor Data, Equations, and Natural Language. CoRR Liu Yang, Tingwei Meng, Siting Liu, Stanley J. Osher
2022	J	`jnl`	Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models. SIAM J. Sci. Comput. Liu Yang, Constantinos Daskalakis, George E. Karniadakis
2022	J	`jnl`	Learning functional priors and posteriors from data and physics. J. Comput. Phys. Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis
2022	J	`jnl`	Potential Flow Generator With L IEEE Trans. Neural Networks Learn. Syst. Liu Yang, George Em Karniadakis
2021	J	`jnl`	B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. J. Comput. Phys. Liu Yang, Xuhui Meng, George Em Karniadakis
2021	J	`jnl`	Learning Functional Priors and Posteriors from Data and Physics. CoRR Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis
2021	J	`jnl`	Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates. CoRR Liu Yang, Tingwei Meng, George Em Karniadakis
2021	J	`jnl`	Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-Informed Neural Networks. SIAM J. Sci. Comput. Xiaoli Chen, Liu Yang, Jinqiao Duan, George Em Karniadakis
2020	J	`jnl`	B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data. CoRR Liu Yang, Xuhui Meng, George Em Karniadakis
2020	J	`jnl`	Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations. CoRR Liu Yang, Constantinos Daskalakis, George Em Karniadakis
2020	J	`jnl`	Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations. SIAM J. Sci. Comput. Liu Yang, Dongkun Zhang, George Em Karniadakis
2020	J	`jnl`	Reinforcement Learning for Active Flow Control in Experiments. CoRR Dixia Fan, Liu Yang, Michael S. Triantafyllou, George Em Karniadakis
2020	J	`jnl`	Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks. CoRR Xiaoli Chen, Liu Yang, Jinqiao Duan, George Em Karniadakis
2019	—	`conf`	Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC 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
2019	J	`jnl`	Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR 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
2019	J	`jnl`	Neural-net-induced Gaussian process regression for function approximation and PDE solution. J. Comput. Phys. Guofei Pang, Liu Yang, George E. Karniadakis
2019	J	`jnl`	Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models. CoRR Liu Yang, George E. Karniadakis
2018	J	`jnl`	Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors. J. Comput. Phys. Dongkun Zhang, Liu Yang, George E. Karniadakis
2018	J	`jnl`	Neural-net-induced Gaussian process regression for function approximation and PDE solution. CoRR Guofei Pang, Liu Yang, George E. Karniadakis
2018	J	`jnl`	Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations. CoRR Liu Yang, Dongkun Zhang, George E. Karniadakis