Impact of the Input Dataset Size on Accuracy of Reduced Order Models of Rubber-Metal Elements for Static Characteristics

Juraj Dobrovolný; Ján Danko; Martin Bernáth; Tomáš Milesich; Igor Kevický; Ľuboš Magdolen

doi:10.2478/scjme-2026-0020

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Impact of the Input Dataset Size on Accuracy of Reduced Order Models of Rubber-Metal Elements for Static Characteristics

Strojnícky časopis - Journal of Mechanical Engineering

Volume 76 (2026): Issue 2 (November 2026)

By: Juraj Dobrovolný, Ján Danko, Martin Bernáth, Tomáš Milesich, Igor Kevický and Ľuboš Magdolen

Open Access

|Nov 2026

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Abstract

The work deals with the impact of the input dataset size on accuracy of reduced order models (ROM) of rubber-metal elements (RME) for static characteristics. First chapter describes the size and structure of the input dataset. Second chapter focuses on the process of the input dataset generation. Third chapter describes the generation process of ROM of RME with given neural network (NN) settings. Fourth chapter compares the static stiffnesses of generated ROM models with finite element method (FEM) models with corresponding shapes for input datasets with different sizes.

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Articles in this issue

DOI: https://doi.org/10.2478/scjme-2026-0020 | Journal eISSN: 2450-5471 | Journal ISSN: 0039-2472

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Language: English

Page range: 46 - 53

Published online: Nov 12, 2026

Published in print: Nov 1, 2026

Published by: Slovak University of Technology in Bratislava

In partnership with: Paradigm Publishing Services

Keywords:

Related subjects:

Mechanical engineering,

Fundamentals of mechanical engineering,

Mechanics

© 2026 Juraj Dobrovolný, Ján Danko, Martin Bernáth, Tomáš Milesich, Igor Kevický, Ľuboš Magdolen, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 76 (2026): Issue 2 (November 2026)