Open AccessThe algorithm for generating the training set for the problem of elastoplastic deformation of the metal rod
Author(s) -
Mykhailo Seleznov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2070/1/012042
Subject(s) - set (abstract data type) , simple (philosophy) , deformation (meteorology) , range (aeronautics) , algorithm , computer science , work (physics) , quality (philosophy) , action (physics) , training (meteorology) , mathematical optimization , mathematics , materials science , mechanical engineering , engineering , physics , meteorology , composite material , programming language , philosophy , epistemology , quantum mechanics
The paper proposes an algorithm for forming a small training set, which will provide a reasonable quality of a surrogate ML-model for the problem of elastoplastic deformation of a metal rod under the action of a longitudinal load pulse. This dynamic physical problem is computationally simple and convenient for testing various approaches, but at the same time it is physically quite complex, because it contains a significant range of effects. So, the methods tested on this problem can be further applied to other areas. This work demonstrates the possibility of a surrogate ML-model to provide a reasonable prediction quality for a dynamic physical problem with a small training set size.