Physics-aware deep learning framework for linear elasticity
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Abstract (arXiv)
The paper presents an efficient and robust data-driven deep learning (DL) computational framework developed for linear continuum elasticity problems. The methodology is based on the fundamentals of the Physics Informed Neural Networks (PINNs). For an accurate representation of the field variables, a multi-objective loss function is proposed. It consists of terms corresponding to the residual of the governing partial differential equations (PDE), constitutive relations derived from the governing physics, various boundary conditions, and data-driven physical knowledge fitting terms across randomly selected collocation points in the problem domain. To this end, multiple densely connected independent artificial neural networks (ANNs), each approximating a field variable, are trained to obtain accurate solutions. Several benchmark problems including the Airy solution to elasticity and the Kirchhoff-Love plate problem are solved. Performance in terms of accuracy and robustness illustrates the superiority of the current framework showing excellent agreement with analytical solutions. The present work combines the benefits of the classical methods depending on the physical information available in analytical relations with the superior capabilities of the DL techniques in the data-driven construction of lightweight, yet accurate and robust neural networks. The models developed herein can significantly boost computational speed using minimal network parameters with easy adaptability in different computational platforms.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 122 references that are in the map. First posted on arXiv 19 Feb 2023.
Builds on
32 of 122 references.
References · 32
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20 more
- 2022Error estimates for physics informed neural networks approximating the Navier-Stokes equationscited by 63
- 2021When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?cited by 57
- 2021Evolutional Deep Neural Networkcited by 48
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.