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Podatki numeričnih simulacij, obravnavani v članku Surrogate model for FEA analysis and damage calculation used for exhaust system validation

Year: 2026

Type: article

Abstract: The research data consist of the training and validation datasets and the source code used for building the surrogate model. They include both input and output data as well as surrogate model parameters. The data were generated for the purpose of training and validating a surrogate model (neural network) for predicting the fatigue life of an exhaust system. The goal of the methodology using a surrogate model is to replace time consuming finite element modeling and fatigue life calculations with a computationally simpler approach for determining the location and magnitude of the maximum damage.

Source: Repository of the University of Ljubljana (University of Ljubljana)

Authors: Zaletel, Jan, Oman, Simon, Nagode, Marko, Klemenc, Jernej

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7149876218

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