A Hierarchical A-Posteriori Error Estimatorfor the Reduced Basis Method
This work addresses the need for tight error estimation in reduced-order modeling for parameterized variational problems, offering an alternative to residual-based estimators that may become infeasible when the inf-sup constant is small.
The paper proposes a hierarchical a-posteriori error estimator for the Reduced Basis Method that evaluates the difference between two reduced approximations of different accuracy, achieving efficiency indices close to one when the Kolmogorov N-width decays fast. Numerical experiments demonstrate its effectiveness for both basis construction via the weak Greedy algorithm and online certification, particularly when the inf-sup constant is small.
In this contribution we are concerned with tight a posteriori error estimation for projection based model order reduction of $\inf$-$\sup$ stable parameterized variational problems. In particular, we consider the Reduced Basis Method in a Petrov-Galerkin framework, where the reduced approximation spaces are constructed by the (weak) Greedy algorithm. We propose and analyze a hierarchical a posteriori error estimator which evaluates the difference of two reduced approximations of different accuracy. Based on the a priori error analysis of the (weak) Greedy algorithm, it is expected that the hierarchical error estimator is sharp with efficiency index close to one, if the Kolmogorov N-with decays fast for the underlying problem and if a suitable saturation assumption for the reduced approximation is satisfied. We investigate the tightness of the hierarchical a posteriori estimator both from a theoretical and numerical perspective. For the respective approximation with higher accuracy we study and compare basis enrichment of Lagrange- and Taylor-type reduced bases. Numerical experiments indicate the efficiency for both, the construction of a reduced basis using the hierarchical error estimator in a weak Greedy algorithm, and for tight online certification of reduced approximations. This is particularly relevant in cases where the $\inf$-$\sup$ constant may become small depending on the parameter. In such cases a standard residual-based error estimator -- complemented by the successive constrained method to compute a lower bound of the parameter dependent $\inf$-$\sup$ constant -- may become infeasible.