Chengping Rao

According to our database, Chengping Rao authored at least 12 papers between 2020 and 2023.

Collaborative distances:

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

2023

PhySR: Physics-informed deep super-resolution for spatiotemporal data.

Pu Ren, Chengping Rao, Yang Liu, Zihan Ma, Qi Wang, Jian-Xun Wang, Hao Sun

J. Comput. Phys., November, 2023

Encoding physics to learn reaction-diffusion processes.

Chengping Rao, Pu Ren, Qi Wang, Oral Buyukozturk, Hao Sun, Yang Liu

Nat. Mac. Intell., July, 2023

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

Pu Ren, Chengping Rao, Hao Sun, Yang Liu

CoRR, 2023

2022

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

Pu Ren, Chengping Rao, Su Chen, Jian-sun Wang, Hao Sun, Yang Liu

CoRR, 2022

Physics-informed Deep Super-resolution for Spatiotemporal Data.

Pu Ren, Chengping Rao, Yang Liu, Zihan Ma, Qi Wang, Jian-Xun Wang, Hao Sun

CoRR, 2022

Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning.

Chengping Rao, Pu Ren, Yang Liu, Hao Sun

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

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

Pu Ren, Chengping Rao, Yang Liu, Jian-Xun Wang, Hao Sun

CoRR, 2021

Embedding Physics to Learn Spatiotemporal Dynamics from Sparse Data.

Chengping Rao, Hao Sun, Yang Liu

CoRR, 2021

Hard Encoding of Physics for Learning Spatiotemporal Dynamics.

Chengping Rao, Hao Sun, Yang Liu

CoRR, 2021

2020

Physics informed deep learning for computational elastodynamics without labeled data.

Chengping Rao, Hao Sun, Yang Liu

CoRR, 2020

Physics-informed deep learning for incompressible laminar flows.

Chengping Rao, Hao Sun, Yang Liu

CoRR, 2020

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

Chengping Rao, Yang Liu

CoRR, 2020