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Kailiang Wu

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Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems

Apr 15, 2023
Ce Zhang, Kailiang Wu, Zhihai He

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Deep-OSG: A deep learning approach for approximating a family of operators in semigroup to model unknown autonomous systems

Feb 07, 2023
Junfeng Chen, Kailiang Wu

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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

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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

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

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

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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

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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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