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Solving the Inverse Potential Problem in the Parabolic Equation by the Deep Neural Networks Method

Solving the Inverse Potential Problem in the Parabolic Equation by the Deep Neural Networks Method

Year:    2024

Author:    Mengmeng Zhang, Zhidong Zhang

CSIAM Transactions on Applied Mathematics, Vol. 5 (2024), Iss. 4 : pp. 852–883

Abstract

In this work, we consider an inverse potential problem in the parabolic equation, where the unknown potential is a space-dependent function and the used measurement is the final time data. The unknown potential in this inverse problem is parameterized by deep neural networks (DNNs) for the reconstruction scheme. First, the uniqueness of the inverse problem is proved under some regularities assumption on the input sources. Then we propose a new loss function with regularization terms depending on the derivatives of the residuals for partial differential equations (PDEs) and the measurements. These extra terms effectively induce higher regularity in solutions so that the ill-posedness of the inverse problem can be handled. Moreover, we establish the corresponding generalization error estimates rigorously. Our proofs exploit the conditional stability of the classical linear inverse source problems, and the mollification on the noisy measurement data which is set to reduce the perturbation errors. Finally, the numerical algorithm and some numerical results are provided.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/csiam-am.SO-2023-0035

CSIAM Transactions on Applied Mathematics, Vol. 5 (2024), Iss. 4 : pp. 852–883

Published online:    2024-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    32

Keywords:    Inverse potential problem deep neural networks uniqueness generalization error estimates numerical reconstruction.

Author Details

Mengmeng Zhang

Zhidong Zhang