Abstract:We develop an interpolation-based modeling framework for parameter-dependent partial differential equations arising in control, inverse problems, and uncertainty quantification. The solution is discretized in the physical domain using finite element methods, while the dependence on a finite-dimensional parameter is approximated separately. We establish existence, uniqueness, and regularity of the parametric solution and derive rigorous error estimates that explicitly quantify the interplay between spatial discretization and parameter approximation.
In low-dimensional parameter spaces, classical interpolation schemes yield algebraic convergence rates based on Sobolev regularity in the parameter variable. In higher-dimensional parameter spaces, we replace classical interpolation by extreme learning machine (ELM) surrogates and obtain error bounds under explicit approximation and stability assumptions. The proposed framework is applied to inverse problems in quantitative photoacoustic tomography, where we derive potential and parameter reconstruction error estimates and demonstrate substantial computational savings compared to standard approaches, without sacrificing accuracy.
| Subjects: | Numerical Analysis (math.NA); Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.14757 [math.NA] |
| (or arXiv:2602.14757v2 [math.NA] for this version) | |
| https://doi.org/10.48550/arXiv.2602.14757 arXiv-issued DOI via DataCite |
From: Karl Larsson [view email]
[v1]
Mon, 16 Feb 2026 14:01:50 UTC (1,932 KB)
[v2]
Fri, 17 Apr 2026 05:22:28 UTC (2,377 KB)
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