Yang Liu

Orcid: 0000-0003-0127-4030

Affiliations:
  • University of the Chinese Academy of Sciences, School of Engineering Sciences, Beijing, China
  • Northeastern University, Department of Mechanical and Industrial Engineering, Boston, MA, USA
  • Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, USA (2015 - 2017)
  • Columbia University, Department of Civil Engineering and Engineering Mechanics, New York, NY, USA (PhD 2015)


According to our database1, Yang Liu authored at least 21 papers between 2014 and 2023.

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

Timeline

Legend:

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Links

Online presence:

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Bibliography

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

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

Physics-informed Deep Super-resolution for Spatiotemporal Data.
CoRR, 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
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
Physics informed deep learning for computational elastodynamics without labeled data.
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

Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization.
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

2014
Automated modeling of random inclusion composites.
Eng. Comput., 2014


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