2025/07/08 by Thomas Führer, Führer, Thomas, Sergio Rojas +1 · 1 citation
Engineering · Physics and Astronomy · #65M60 #65M75 #65N15 #Advanced Numerical Methods in Computational Mathematics #FOS: Mathematics #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Topology Optimization in Engineering
paper · pdf · doi:10.48550/arxiv.2507.06017
openalex publication_date 2025/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In a general setting, we study a posteriori estimates used in finite element analysis to measure the error between a solution and its approximation. The latter is not necessarily generated by a finite element method. We show that the error is equivalent to the sum of two residuals provided that the underlying variational formulation is well posed. The first contribution is the projection of the residual to a finite-dimensional space and is therefore computable, while the second one can be reliably estimated by a computable upper bound in many practical scenarios. Assuming sufficiently accurate quadrature, our findings can be used to estimate the error of, e.g., neural network outputs. Two important applications can be considered during optimization: first, the estimators are used to monitor the error in each solver step, or, second, the two estimators are included in the loss functional, and therefore provide control over the error. As a model problem, we consider a second-order elliptic partial differential equation and discuss different variational formulations thereof, including several options to include boundary conditions in the estimators. Various numerical experiments are presented to validate our findings.