151 citations · 250 across the 5 of their papers we have counts for
5 papers
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
Data-driven discovery of PDEs has made tremendous progress recently, and many canonical PDEs have been discovered successfully for proof-of-concept. However, determining the most p…
Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network
Pengfei Tang, Junsheng Zeng, Dongxiao Zhang +1
Particle settling in inclined channels is an important phenomenon that occurs during hydraulic fracturing of shale gas production. Generally, in order to accurately simulate the la…
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
Junsheng Zeng, Hao Xu, Yuntian Chen +1
Although deep-learning has been successfully applied in a variety of science and engineering problems owing to its strong high-dimensional nonlinear mapping capability, it is of li…
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Yuntian Chen, Dou Huang, Dongxiao Zhang +4
Machine learning models have been successfully used in many scientific and engineering fields. However, it remains difficult for a model to simultaneously utilize domain knowledge…
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data
Hao Xu, Dongxiao Zhang, Junsheng Zeng
Data-driven methods have recently made great progress in the discovery of partial differential equations (PDEs) from spatial-temporal data. However, several challenges remain to be…