7 citations · 13 across the 4 of their papers we have counts for
4 papers · 1 filter
About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks
Vasiliy A. Es'kin, Alexey O. Malkhanov, Mikhail E. Smorkalov
The article is devoted to the study of neural networks with one hidden layer and a modified activation function for solving physical problems. A rectified sigmoid activation functi…
Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?
Vasiliy A. Es'kin, Alexey O. Malkhanov, Mikhail E. Smorkalov
The article discusses the development of various methods and techniques for initializing and training neural networks with a single hidden layer, as well as training a separable ph…
Separable Physics-Informed Neural Networks for the solution of elasticity problems
Vasiliy A. Es'kin, Danil V. Davydov, Julia V. Gur'eva +2
A method for solving elasticity problems based on separable physics-informed neural networks (SPINN) in conjunction with the deep energy method (DEM) is presented. Numerical experi…
About optimal loss function for training physics-informed neural networks under respecting causality
Vasiliy A. Es'kin, Danil V. Davydov, Ekaterina D. Egorova +3
A method is presented that allows to reduce a problem described by differential equations with initial and boundary conditions to the problem described only by differential equatio…