A robust error estimator and a residual-free error indicator for reduced basis methods

Yanlai Chen, Jiahua Jiang, Akil Narayan

Research output: Contribution to journalArticlepeer-review


The Reduced Basis Method (RBM) is a rigorous model reduction approach for solving parameterizedpartial differential equations. It identifies a low-dimensional subspace for approximation of the parametric solution manifold that is embedded in high-dimensional space. A reduced order model is subsequently constructed in this subspace. RBM relies on residual-based error indicators or a posteriori error bounds to guide construction of the reduced solution subspace, to serve as a stopping criteria, and to certify the resulting surrogate solutions. Unfortunately, it is well-known that the standard algorithm for residual norm computation suffers from premature stagnation at the level of the square root of machine precision. In this paper, we develop two alternatives to the standard offline phase of reduced basis algorithms. First, we design a robust strategy for computation of residual error indicators that allows RBM algorithms to enrich the solution subspace with accuracy beyond root machine precision. Secondly, we propose a new error indicator based on the Lebesgue function in interpolation theory. This error indicator does not require computation of residual norms, and instead only requires the ability to compute the RBM solution. This residual-free indicator is rigorous in that it bounds the error committed by the RBM approximation, but up to an uncomputable multiplicative constant. Because of this, the residual-free indicator is effective in choosing snapshots during the offline RBM phase, but cannot currently be used to certify error that the approximation commits. However, it circumvents the need for a posteriori analysis of numerical methods, and therefore can be effective on problems where such a rigorous estimate is hard to derive.
Original languageEnglish
Pages (from-to)1963-1979
Number of pages17
JournalComputers & Mathematics with Applications
Issue number7
Publication statusPublished - 2019


  • Reduced basis method
  • Greedy algorithms
  • Residual-based error estimation
  • Error stagnation


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