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Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space


Jun 07, 2021
Zhen Chen, Victor Churchill, Kailiang Wu, Dongbin Xiu


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Methods to Recover Unknown Processes in Partial Differential Equations Using Data


Mar 05, 2020
Zhen Chen, Kailiang Wu, Dongbin Xiu

* 21 pages, 11 figures 

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A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data


Feb 11, 2020
Jun Hou, Tong Qin, Kailiang Wu, Dongbin Xiu


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Data-Driven Deep Learning of Partial Differential Equations in Modal Space


Oct 18, 2019
Kailiang Wu, Dongbin Xiu

* Minor notational changes 

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Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data


May 24, 2019
Kailiang Wu, Tong Qin, Dongbin Xiu

* 24 pages, 16 figures 

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Data Driven Governing Equations Approximation Using Deep Neural Networks


Nov 13, 2018
Tong Qin, Kailiang Wu, Dongbin Xiu


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Numerical Aspects for Approximating Governing Equations Using Data


Sep 24, 2018
Kailiang Wu, Dongbin Xiu

* 26 pages, 17 figures 

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An Explicit Neural Network Construction for Piecewise Constant Function Approximation


Aug 22, 2018
Kailiang Wu, Dongbin Xiu


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