Search Results for author: Mikhail E. Smorkalov

Found 3 papers, 0 papers with code

Separable Physics-Informed Neural Networks for the solution of elasticity problems

no code implementations24 Jan 2024 Vasiliy A. Es'kin, Danil V. Davydov, Julia V. Gur'eva, Alexey O. Malkhanov, Mikhail E. Smorkalov

A method for solving elasticity problems based on separable physics-informed neural networks (SPINN) in conjunction with the deep energy method (DEM) is presented.

About optimal loss function for training physics-informed neural networks under respecting causality

no code implementations5 Apr 2023 Vasiliy A. Es'kin, Danil V. Davydov, Ekaterina D. Egorova, Alexey O. Malkhanov, Mikhail A. Akhukov, Mikhail E. Smorkalov

The advantage of using the modified problem for physics-informed neural networks (PINNs) methodology is that it becomes possible to represent the loss function in the form of a single term associated with differential equations, thus eliminating the need to tune the scaling coefficients for the terms related to boundary and initial conditions.

On Scale-out Deep Learning Training for Cloud and HPC

no code implementations24 Jan 2018 Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey

The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes.

Philosophy

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