Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks

arXiv:2602.14757v2 Announce Type: replace-cross 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.

Related

• Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
• Fast training of accurate physics-informed neural networks without gradient descent
• LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
• AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling
• Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
• Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations

Source: arXiv cs.LG | 2026-04-20