An adaptive reduced-basis trust-region Gauss-Newton method is extended to parabolic parameter identification, with POD-based enrichment, achieving 5 to 18x speedups in four reaction-diffusion tests.
A numerically stable a posteriori error estimator for reduced basis approximations of elliptic equations
1 Pith paper cite this work, alongside 21 external citations. Polarity classification is still indexing.
abstract
The Reduced Basis (RB) method is a well established method for the model order reduction of problems formulated as parametrized partial differential equations. One crucial requirement for the application of RB schemes is the availability of an a posteriori error estimator to reliably estimate the error introduced by the reduction process. However, straightforward implementations of standard residual based estimators show poor numerical stability, rendering them unusable if high accuracy is required. In this work we propose a new algorithm based on representing the residual with respect to a dedicated orthonormal basis, which is both easy to implement and requires little additional computational overhead. A numerical example is given to demonstrate the performance of the proposed algorithm.
citation-role summary
citation-polarity summary
fields
math.NA 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Adaptive Reduced Basis Trust Region Methods for Parabolic Inverse Problems
An adaptive reduced-basis trust-region Gauss-Newton method is extended to parabolic parameter identification, with POD-based enrichment, achieving 5 to 18x speedups in four reaction-diffusion tests.