Numerical Approaches for Identifying the Time-Dependent Potential Coefficient in the Diffusion Equation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917073378082816 |
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| author | Altybay, Arshyn Ruzhansky, Michael |
| author_facet | Altybay, Arshyn Ruzhansky, Michael |
| contents | We address the inverse problem of identifying a time-dependent potential coefficient in a one-dimensional diffusion equation subject to Dirichlet boundary conditions and a nonlocal integral overdetermination constraint reflecting spatially averaged measurements. After establishing well-posedness for the forward problem and deriving an a priori estimate that ensures uniqueness and continuous dependence on the data, we prove existence and uniqueness for the inverse problem. To compute numerically the unknown coefficient, we propose and compare three numerical methods: an integration-based scheme, a Newton-Raphson iterative solver, and a physics-informed neural network (PINN). Numerical experiments on both exact and noisy data demonstrate the accuracy, robustness, and efficiency of each approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Numerical Approaches for Identifying the Time-Dependent Potential Coefficient in the Diffusion Equation Altybay, Arshyn Ruzhansky, Michael Numerical Analysis Mathematical Physics We address the inverse problem of identifying a time-dependent potential coefficient in a one-dimensional diffusion equation subject to Dirichlet boundary conditions and a nonlocal integral overdetermination constraint reflecting spatially averaged measurements. After establishing well-posedness for the forward problem and deriving an a priori estimate that ensures uniqueness and continuous dependence on the data, we prove existence and uniqueness for the inverse problem. To compute numerically the unknown coefficient, we propose and compare three numerical methods: an integration-based scheme, a Newton-Raphson iterative solver, and a physics-informed neural network (PINN). Numerical experiments on both exact and noisy data demonstrate the accuracy, robustness, and efficiency of each approach. |
| title | Numerical Approaches for Identifying the Time-Dependent Potential Coefficient in the Diffusion Equation |
| topic | Numerical Analysis Mathematical Physics |
| url | https://arxiv.org/abs/2511.08302 |