Adaptive regularisation for PDE-constrained optimal control

PDE-constrained optimal control problems require regularisation to ensure well-posedness, introducing small perturbations that make the solutions challenging to approximate accurately. We propose a finite element approach that couples both regularisation and discretisation adaptivity, varying both the regularisation parameter and mesh-size locally based on rigorous a posteriori error estimates aiming to dynamically balance induced regularisation and discretisation errors, offering a robust and efficient method for solving these problems. We demonstrate the efficacy of our analysis with several numerical experiments.

Authors

Publication Details

Journal
Journal of Computational and Applied Mathematics
Published
2025-03-30
DOI
https://doi.org/10.1016/j.cam.2025.116651
Citations
2
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
7.38
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article

Adaptive regularisation for PDE-constrained optimal control

Tristan Pryer, Jenny Power
2 citations
Journal of Computational and Applied Mathematics
Numerical methods in inverse problems
7.38
article

Adaptive regularisation for PDE-constrained optimal control

Tristan Pryer, Jenny Power
article en
2 citations

Abstract

PDE-constrained optimal control problems require regularisation to ensure well-posedness, introducing small perturbations that make the solutions challenging to approximate accurately. We propose a finite element approach that couples both regularisation and discretisation adaptivity, varying both the regularisation parameter and mesh-size locally based on rigorous a posteriori error estimates aiming to dynamically balance induced regularisation and discretisation errors, offering a robust and efficient method for solving these problems. We demonstrate the efficacy of our analysis with several numerical experiments.

Journal of Computational and Applied MathematicsVol. 470
Openalex Percentile: Top 4%
Numerical methods in inverse problems
7.38
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