Previsão da trajetória de um sistema massa-mola-amortecedor não-linear por meio de redes neurais lagrangianas

Abstract

In this work, Lagrangian Neural Networks (LNNs) were applied to determine the Lagrangian of a mechanical system and to predict its trajectory in the state space. Neural networks, Lagrangian mechanics, Newtonian mechanics, dynamic models, numerical integration, parametric optimization, and evaluation criteria were applied for the case study of a non-linear mass-spring-damper system. The trajectories predicted by means of the Lagrangian Neural Networks were compared to the data generated by means of computer simulations of Newton’s second law. The main conclusion of this work is that it is feasible to predict the trajectory of this system by means of Lagrangian Neural Networks. Furthermore, the flexibility of the LNN allowed the modeling of non-linear dynamics in the system.