Imaging Anisotropic Conductivity from Internal Measurements with Mixed Least-Squares Deep Neural Networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912139112873984 |
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| author | Cen, Siyu Jin, Bangti Li, Xiyao Zhou, Zhi |
| author_facet | Cen, Siyu Jin, Bangti Li, Xiyao Zhou, Zhi |
| contents | In this work we develop a novel algorithm, termed as mixed least-squares deep neural network (MLS-DNN), to recover an anisotropic conductivity tensor from the internal measurements of the solutions. It is based on applying the least-squares formulation to the mixed form of the elliptic problem, and approximating the internal flux and conductivity tensor simultaneously using deep neural networks. We provide error bounds on the approximations obtained via both population and empirical losses. The analysis relies on the canonical source condition, approximation theory of deep neural networks and statistical learning theory. We also present multiple numerical experiments to illustrate the performance of the method, and conduct a comparative study with the standard Galerkin finite element method and physics informed neural network. The results indicate that the method can accurately recover the anisotropic conductivity in both two- and three-dimensional cases, up to 10\% noise in the data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00527 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Imaging Anisotropic Conductivity from Internal Measurements with Mixed Least-Squares Deep Neural Networks Cen, Siyu Jin, Bangti Li, Xiyao Zhou, Zhi Numerical Analysis In this work we develop a novel algorithm, termed as mixed least-squares deep neural network (MLS-DNN), to recover an anisotropic conductivity tensor from the internal measurements of the solutions. It is based on applying the least-squares formulation to the mixed form of the elliptic problem, and approximating the internal flux and conductivity tensor simultaneously using deep neural networks. We provide error bounds on the approximations obtained via both population and empirical losses. The analysis relies on the canonical source condition, approximation theory of deep neural networks and statistical learning theory. We also present multiple numerical experiments to illustrate the performance of the method, and conduct a comparative study with the standard Galerkin finite element method and physics informed neural network. The results indicate that the method can accurately recover the anisotropic conductivity in both two- and three-dimensional cases, up to 10\% noise in the data. |
| title | Imaging Anisotropic Conductivity from Internal Measurements with Mixed Least-Squares Deep Neural Networks |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2412.00527 |