About Modifications of the Loss Function for the Causal Training of Physics-Informed Neural Networks

被引：0

作者：

V. A. Es’kin ^{[1
]}

D. V. Davydov ^{[2
]}

E. D. Egorova ^{[3
]}

A. O. Malkhanov ^{[2
]}

M. A. Akhukov ^{[4
]}

M. E. Smorkalov ^{[5
]}

机构：

[1] Department of Radiophysics,

[2] University of Nizhny Novgorod,undefined

[3] Manpower IT Solutions,undefined

[4] Mechanical Engineering Research Institute Russian Academy of Sciences,undefined

[5] Institute of Applied Physics Russian Academy of Sciences,undefined

[6] Huawei Nizhny Novgorod Research Center,undefined

[7] Skolkovo Institute of Science and Technology,undefined

来源：

Doklady Mathematics | 2024年 / 110卷 / Suppl 1期

关键词：

deep learning; physics-informed neural networks; partial differential equations; predictive modeling; computational physics; nonlinear dynamics;

D O I：

10.1134/S106456242460194X

中图分类号：

学科分类号：

摘要：

引用

页码：S172 / S192

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