107 citations · 488 across the 7 of their papers we have counts for
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3 papers · 1 filter
cs.LG2020★ 37 cited
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Hao Xu, Dongxiao Zhang, Nanzhe Wang
Data-driven discovery of partial differential equations (PDEs) has attracted increasing attention in recent years. Although significant progress has been made, certain unresolved i…
physics.comp-ph2020★ 64 cited
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…
cs.NE2020★ 107 cited
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
Hao Xu, Haibin Chang, Dongxiao Zhang
Data-driven methods have recently been developed to discover underlying partial differential equations (PDEs) of physical problems. However, for these methods, a complete candidate…