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February 2, 2025Neurocomputers

Setting up the parameters of dynamic models of the organizational and technological process of railway transport technical vehicles re-pair using Neural ODE

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Authors

ODO.V. DruzhininaIMI.V. MakarenkovaVMV.V. Maksimova

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Overview

Analysis reveals improved parametric identification in railway transport vehicles, highlighting machine learning's role in optimizing operations.

Key Points

  • The implementation of neural ODE optimizes parametric identification for repairing railway transport technical vehicles, enhancing safety and stability.
  • Experimental results indicate refined parameters are achievable through machine learning cycles applied to empirical data.
  • This analysis utilizes dynamic models governed by nonlinear differential equations to address technological processes in transport infrastructure.
  • The approach supports the creation of digital twins, essential for optimizing vehicle numbers and the efficiency of transportation systems.

Cite This Study

Druzhinina et al. (2025) studied this question.

synapsesocial.com/papers/68af6bf77567bf4f94fe95d0https://doi.org/10.18127/j19998554-202502-05
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