Hao Sun

Orcid: 0000-0002-5145-3259

Affiliations:
  • Renmin University of China, Gaoling School of Artificial Intelligence, Beijing, China
  • Northeastern University, Department of Civil and Environmental Engineering, Lab for Infrastructure Sensing and Data Science, Boston, MA, USA
  • Massachusetts Institute of Technology, Cambridge, MA, USA
  • Columbia University, New York, NY, USA (PhD 2014)


According to our database1, Hao Sun authored at least 37 papers between 2015 and 2024.

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

Timeline

Legend:

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Bibliography

2024
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain.
Comput. Phys. Commun., 2024

Discovering symbolic expressions with parallelized tree search.
CoRR, 2024

Reasoning-Enhanced Object-Centric Learning for Videos.
CoRR, 2024

Vision-based Discovery of Nonlinear Dynamics for 3D Moving Target.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Reinforcement Symbolic Regression Machine.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
PhySR: Physics-informed deep super-resolution for spatiotemporal data.
J. Comput. Phys., November, 2023

Encoding physics to learn reaction-diffusion processes.
Nat. Mac. Intell., July, 2023

AI-accelerated Discovery of Altermagnetic Materials.
CoRR, 2023

TLNets: Transformation Learning Networks for long-range time-series prediction.
CoRR, 2023

RSRM: Reinforcement Symbolic Regression Machine.
CoRR, 2023

Physics-informed neural network for seismic wave inversion in layered semi-infinite domain.
CoRR, 2023

Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Forecasting of nonlinear dynamics based on symbolic invariance.
Comput. Phys. Commun., 2022

SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain.
CoRR, 2022

A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language.
CoRR, 2022

Multimodal foundation models are better simulators of the human brain.
CoRR, 2022

Physics-informed Deep Super-resolution for Spatiotemporal Data.
CoRR, 2022

Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning.
CoRR, 2022

Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Distilling Governing Laws and Source Input for Dynamical Systems from Videos.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model.
CoRR, 2021

PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs.
CoRR, 2021

Embedding Physics to Learn Spatiotemporal Dynamics from Sparse Data.
CoRR, 2021

Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos.
CoRR, 2021

Hard Encoding of Physics for Learning Spatiotemporal Dynamics.
CoRR, 2021

Physics-informed Spline Learning for Nonlinear Dynamics Discovery.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020
Extracting full-field subpixel structural displacements from videos via deep learning.
CoRR, 2020

Incremental Bayesian tensor learning for structural monitoring data imputation and response forecasting.
CoRR, 2020

Physics informed deep learning for computational elastodynamics without labeled data.
CoRR, 2020

Sparse representation for damage identification of structural systems.
CoRR, 2020

Deep learning of physical laws from scarce data.
CoRR, 2020

Physics-informed deep learning for incompressible laminar flows.
CoRR, 2020

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures.
CoRR, 2020

2019
Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling.
CoRR, 2019

2015
Statistical Regularization for Identification of Structural Parameters and External Loadings Using State Space Models.
Comput. Aided Civ. Infrastructure Eng., 2015

A Hybrid Optimization Algorithm with Bayesian Inference for Probabilistic Model Updating.
Comput. Aided Civ. Infrastructure Eng., 2015


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