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. Druzhinina IMI.V. Makarenkova VMV.V. Maksimova

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Implication

This analysis demonstrates refined parametric identification in dynamic models of railway infrastructure using machine learning, suggesting enhanced monitoring capabilities.

Key Points

Implementation of machine learning cycles produces refined parameter sets for dynamic models of transport infrastructure, improving efficiency.
Using neural differential equations, the article highlights the adjustment of repair parameters for railway transport technical vehicles, enhancing operational stability.
Conducted computer experiments validate the proposed parameter tuning algorithm, utilizing a deep learning framework for effective model adjustment.
This approach supports the development of digital twins, optimizing vehicle numbers and enhancing transportation processes in the railway sector.

Cite This Study

Druzhinina et al. (2025) studied this question.

synapsesocial.com/papers/68af4ec6ad7bf08b1ead7e82 https://doi.org/10.18127/j19998554-202502-05

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Implication

This analysis demonstrates refined parametric identification in dynamic models of railway infrastructure using machine learning, suggesting enhanced monitoring capabilities.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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