Zheyuan Hu

According to our database, Zheyuan Hu authored at least 15 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Deep Neural networks for solving high-dimensional parabolic partial differential equations.

Wenzhong Zhang, Zheyuan Hu, Wei Cai, George E. Karniadakis

CoRR, January, 2026

State-space models are accurate and efficient neural operators for dynamical systems.

Zheyuan Hu, Nazanin Ahmadi Daryakenari, Qianli Shen, Kenji Kawaguchi, George Em Karniadakis

Neural Networks, 2026

2025

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations.

Zheyuan Hu, Zhongqiang Zhang, George E. Karniadakis, Kenji Kawaguchi

SIAM J. Sci. Comput., 2025

Bias-Variance Trade-Off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs.

Zheyuan Hu, Zhouhao Yang, Yezhen Wang, George E. Karniadakis, Kenji Kawaguchi

SIAM J. Sci. Comput., 2025

Tensor neural networks for high-dimensional Fokker-Planck equations.

Taorui Wang, Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang, George Em Karniadakis

Neural Networks, 2025

DeepOMamba: State-space model for spatio-temporal PDE neural operator learning.

Zheyuan Hu, Qianying Cao, Kenji Kawaguchi, George Em Karniadakis

J. Comput. Phys., 2025

2024

Tackling the curse of dimensionality with physics-informed neural networks.

Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi

Neural Networks, 2024

Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks.

Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang, George Em Karniadakis

CoRR, 2024

Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Levy Equations.

Zheyuan Hu, Zhongqiang Zhang, George Em Karniadakis, Kenji Kawaguchi

CoRR, 2024

Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators.

Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology.

Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi

Eng. Appl. Artif. Intell., November, 2023

Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks.

Zheyuan Hu, Zekun Shi, George Em Karniadakis, Kenji Kawaguchi

CoRR, 2023

D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory.

Tianbo Li, Min Lin, Zheyuan Hu, Kunhao Zheng, Giovanni Vignale, Kenji Kawaguchi, A. H. Castro Neto, Kostya S. Novoselov, Shuicheng Yan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi

SIAM J. Sci. Comput., 2022

2020

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction.

Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, Cheekong Lee

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020