# L1-ROC and R2-ROC: L1- and R2-based Reduced Over-Collocation methods for parametrized nonlinear partial differential equations

## TL;DR

This paper introduces L1-ROC and R2-ROC, innovative reduced over-collocation methods that enhance efficiency and stability in solving parametrized nonlinear PDEs, overcoming limitations of traditional empirical interpolation methods.

## Contribution

The paper develops two new reduced over-collocation methods, L1-ROC and R2-ROC, which improve online efficiency and stability for nonlinear pPDEs by extending EIM with strategic collocation and parameter selection.

## Key findings

- High efficiency and accuracy demonstrated in numerical tests
- Methods outperform traditional EIM-based approaches
- Stable performance across different nonlinear problems

## Abstract

The onerous task of repeatedly resolving certain parametrized partial differential equations (pPDEs) in, e.g. the optimization context, makes it imperative to design vastly more efficient numerical solvers without sacrificing any accuracy. The reduced basis method (RBM) presents itself as such an option. With a mathematically rigorous error estimator, RBM seeks a surrogate solution in a carefully-built subspace of the parameter-induced high fidelity solution manifold. It can improve efficiency by several orders of magnitudes leveraging an offline-online decomposition procedure. However, this decomposition, usually through the empirical interpolation method (EIM) when the PDE is nonlinear or its parameter dependence nonaffine, is either challenging to implement, or severely degrading to the online efficiency. In this paper, we augment and extend the EIM approach in the context of solving pPDEs in two different ways, resulting in the Reduced Over-Collocation methods (ROC). These are stable and capable of avoiding the efficiency degradation inherent to a direct application of EIM. There are two ingredients of these methods. First is a strategy to collocate at about twice as many locations as the number of bases for the surrogate space. The second is an efficient approach for the strategic selection of the parameter values to build the reduced solution space for which we study two choices, a recent empirical L1 approach and a new indicator based on the reduced residual. Together, these two ingredients render the schemes, L1-ROC and R2-ROC, online efficient and immune from the efficiency degradation of EIM for nonlinear and nonaffine problems offline and online. Numerical tests on three different families of nonlinear problems demonstrate the high efficiency and accuracy of these new algorithms and their superior stability performance.