Conditional well-posedness and data-driven method for identifying the dynamic source in a coupled diffusion system from one single boundary measurement
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917664282116096 |
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| author | Sun, Chunlong Zhang, Mengmeng Zhang, Zhidong |
| author_facet | Sun, Chunlong Zhang, Mengmeng Zhang, Zhidong |
| contents | This work considers the inverse dynamic source problem arising from the time-domain fluorescence diffuse optical tomography (FDOT). We recover the dynamic distributions of fluorophores in biological tissue by the one single boundary measurement in finite time domain. We build the uniqueness theorem of this inverse problem. After that, we introduce a weighted norm and establish the conditional stability of Lipschitz type for the inverse problem by this weighted norm. The numerical inversions are considered under the framework of the deep neural networks (DNNs). We establish the generalization error estimates rigorously derived from Lipschitz conditional stability of inverse problem. Finally, we propose the reconstruction algorithms and give several numerical examples illustrating the performance of the proposed inversion schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07616 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conditional well-posedness and data-driven method for identifying the dynamic source in a coupled diffusion system from one single boundary measurement Sun, Chunlong Zhang, Mengmeng Zhang, Zhidong Numerical Analysis Mathematical Physics Analysis of PDEs This work considers the inverse dynamic source problem arising from the time-domain fluorescence diffuse optical tomography (FDOT). We recover the dynamic distributions of fluorophores in biological tissue by the one single boundary measurement in finite time domain. We build the uniqueness theorem of this inverse problem. After that, we introduce a weighted norm and establish the conditional stability of Lipschitz type for the inverse problem by this weighted norm. The numerical inversions are considered under the framework of the deep neural networks (DNNs). We establish the generalization error estimates rigorously derived from Lipschitz conditional stability of inverse problem. Finally, we propose the reconstruction algorithms and give several numerical examples illustrating the performance of the proposed inversion schemes. |
| title | Conditional well-posedness and data-driven method for identifying the dynamic source in a coupled diffusion system from one single boundary measurement |
| topic | Numerical Analysis Mathematical Physics Analysis of PDEs |
| url | https://arxiv.org/abs/2405.07616 |