A comparison between regular and physics-informed neural networks based on a numerical multibody model: a test case for the synthesis of mechanisms

F. M. Bono; L. Radicioni; S. Cinquemani

doi:10.1117/12.2657981

25 April 2023 A comparison between regular and physics-informed neural networks based on a numerical multibody model: a test case for the synthesis of mechanisms

F. M. Bono, L. Radicioni, S. Cinquemani

Author Affiliations +

Proceedings Volume 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE; 124890L (2023) https://doi.org/10.1117/12.2657981
Event: SPIE Smart Structures + Nondestructive Evaluation, 2023, Long Beach, California, United States

Conference Poster

Abstract

Since 2019 researchers in the field of deep learning have been exploring the possibilities of Physics Informed Neural Networks (PINN). The training of regular neural networks (NNs) involved an optimization where the loss function depends exclusively on the dataset available. In PINN this loss function takes into account also the physics of the problem, if it is known and the governing equations are given. This paper explores the advantages of the use of PINNs with respect to regular NNs, in the privileged case where a multibody model is available. However, there is still uncertainty around how much weight should be associated with each of the two losses (data-driven loss and physics loss). Therefore, different weights for the two losses are considered and their effect on the performance of the model is evaluated. The research focuses on the synthesis of a four-bar mechanism for trajectory planning of a point belonging to the connecting rod. The objective is to generate a tool that synthesizes the mechanism topology given the desired trajectory. This preliminary study shows how PINN are suitable to automatize the synthesis of mechanisms, where regular NN would generally fail. Numerical analyses also demonstrate that a PINN learns relations from a physical numerical model in a more efficient way than a traditional NN.

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F. M. Bono, L. Radicioni, and S. Cinquemani "A comparison between regular and physics-informed neural networks based on a numerical multibody model: a test case for the synthesis of mechanisms", Proc. SPIE 12489, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE, 124890L (25 April 2023); https://doi.org/10.1117/12.2657981

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