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math.NA2022★ 73 cited
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
Xiaolong He, Qizhi He, Jiun-Shyan Chen
Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database and bypass the class…
math.NA2022★ 30 cited
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
Yifei Zong, QiZhi He, Alexandre M. Tartakovsky
In this paper, we develop a physics-informed neural network (PINN) model for parabolic problems with a sharply perturbed initial condition. As an example of a parabolic problem, we…