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Yadav, S and Ganesan, S (2022) SPDE-ConvNet: SOLVE SINGULARLY PERTURBED PARTIAL DIFFERENTIAL EQUATION WITH DEEP LEARNING. In: 8th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS Congress 2022, 5- 9 June 2022, Oslo.

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Official URL: https://doi.org/10.23967/eccomas.2022.258


Singularly Perturbed Partial Differential Equations are challenging to solve with conventional numerical techniques such as Finite Element Methods due to the presence of boundary and interior layers. Often the standard numerical solution has spurious oscillations in the vicinity of these layers. Stabilization techniques are employed to eliminate these spurious oscillations in the numerical solution. The accuracy of the stabilization technique depends on a user-chosen stabilization parameter, where an optimal value is challenging to find. In this work, we focus on predicting an optimal value of the stabilization parameter for a stabilization technique called the Streamline Upwind Petrov Galerkin technique for solving singularly perturbed partial differential equations. This paper proposes SPDE-ConvNet, a convolutional neural network for predicting stabilization parameters by minimizing a loss based on the cross-wind derivative term. The proposed technique is compared with the state-of-the-art variational form-based neural network schemes. © 2022, Scipedia S.L. All rights reserved.

Item Type: Conference Paper
Publication: World Congress in Computational Mechanics and ECCOMAS Congress
Publisher: Scipedia S.L.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Deep Learning; Finite Element Method; Singularly Perturbed Partial Differential Equations; Stabilization Technique; Streamline Upwind Petrov Galerkin
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 16 Feb 2023 05:44
Last Modified: 16 Feb 2023 05:44
URI: https://eprints.iisc.ac.in/id/eprint/80335

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