Lagrangian neural networks for nonholonomic mechanics

Viviana Alejandra Díaz, Leandro Martín Salomone and Marcela Zuccalli

Chaos, Solitons & Fractals, 2025, vol. 199, issue P3

Abstract: Lagrangian Neural Networks (LNNs) are a powerful tool for addressing physical systems, particularly those governed by conservation laws. LNNs can parametrize the Lagrangian of a system to predict trajectories with nearly conserved energy. These techniques have proven effective in unconstrained systems as well as those with holonomic constraints. In this work, we adapt LNN techniques to mechanical systems with nonholonomic constraints. We test our approach on some well-known examples with nonholonomic constraints, showing that incorporating these restrictions into the neural network’s learning improves not only trajectory estimation accuracy but also ensures adherence to constraints and exhibits better energy behavior compared to the unconstrained counterpart.

Keywords: Lagrangian neural networks; Nonholonomic mechanics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:199:y:2025:i:p3:s096007792500880x

DOI: 10.1016/j.chaos.2025.116867

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