SenseNet: A Physics-Informed Deep Learning Model for Shape Sensing
Publication: Journal of Engineering Mechanics
Volume 149, Issue 3
Abstract
Shape sensing is an emerging technique for the reconstruction of deformed shapes using data from a discrete network of strain sensors. The prominence is due to its suitability in promising applications such as structural health monitoring in multiple engineering fields and shape capturing in the medical field. In this work, a physics-informed deep learning model, named SenseNet, was developed for shape sensing applications. Unlike existing neural network approaches for shape sensing, SenseNet incorporates the knowledge of the physics of the problem, so its performance does not rely on the choices of the training data. Compared with numerical physics-based approaches, SenseNet is a mesh-free method, and therefore it offers convenience to problems with complex geometries. SenseNet is composed of two parts: a neural network to predict displacements at the given input coordinates, and a physics part to compute the loss using a function incorporated with physics information. The prior knowledge considered in the loss function includes the boundary conditions and physics relations such as the strain–displacement relation, material constitutive equation, and the governing equation obtained from the law of balance of linear momentum. SenseNet was validated with finite-element solutions for cases with nonlinear displacement fields and stress fields using bending and fixed tension tests, respectively, in both two and three dimensions. A study of the sensor density effects illustrated the fact that the accuracy of the model can be improved using a larger amount of strain data. Because general three dimensional governing equations are incorporated in the model, it was found that SenseNet is capable of reconstructing deformations in volumes with reasonable accuracy using just the surface strain data. Hence, unlike most existing models, SenseNet is not specialized for certain types of elements, and can be extended universally for even thick-body applications.
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Data Availability Statement
Some data, models, or code (e.g., finite-element and SenseNet results) that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Financial support for this research was provided by the US National Science Foundation under Contract No. CMMI-1911836 and by the Stanford UPS Endowment Fund.
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Journal of Engineering Mechanics
Volume 149 • Issue 3 • March 2023
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© 2023 American Society of Civil Engineers.
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Received: Aug 7, 2022
Accepted: Nov 1, 2022
Published online: Jan 5, 2023
Published in print: Mar 1, 2023
Discussion open until: Jun 5, 2023
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Cited by
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