SetPINNs: Set-based Physics-informed Neural Networks

• URL: http://arxiv.org/abs/2409.20206v1
• Date: Mon, 30 Sep 2024 11:41:58 GMT
• Title: SetPINNs: Set-based Physics-informed Neural Networks
• Authors: Mayank Nagda, Phil Ostheimer, Thomas Specht, Frank Rhein, Fabian Jirasek, Marius Kloft, Sophie Fellenz,
• Abstract summary: Physics-Informed Neural Networks (PINNs) have emerged as a promising method for approximating solutions to partial differential equations (PDEs) using deep learning. We propose SetPINNs, a novel approach inspired by Finite Elements Methods from the field of Numerical Analysis. We show that SetPINNs demonstrate superior generalization performance and accuracy across diverse physical systems.
• Score: 19.27512418720287
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Physics-Informed Neural Networks (PINNs) have emerged as a promising method for approximating solutions to partial differential equations (PDEs) using deep learning. However, PINNs, based on multilayer perceptrons (MLP), often employ point-wise predictions, overlooking the implicit dependencies within the physical system such as temporal or spatial dependencies. These dependencies can be captured using more complex network architectures, for example CNNs or Transformers. However, these architectures conventionally do not allow for incorporating physical constraints, as advancements in integrating such constraints within these frameworks are still lacking. Relying on point-wise predictions often results in trivial solutions. To address this limitation, we propose SetPINNs, a novel approach inspired by Finite Elements Methods from the field of Numerical Analysis. SetPINNs allow for incorporating the dependencies inherent in the physical system while at the same time allowing for incorporating the physical constraints. They accurately approximate PDE solutions of a region, thereby modeling the inherent dependencies between multiple neighboring points in that region. Our experiments show that SetPINNs demonstrate superior generalization performance and accuracy across diverse physical systems, showing that they mitigate failure modes and converge faster in comparison to existing approaches. Furthermore, we demonstrate the utility of SetPINNs on two real-world physical systems.

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