Hyper-reduction methods for accelerating nonlinear finite element simulations: open source implementation and reproducible benchmarks

TL;DR

This paper evaluates various hyper-reduction techniques for nonlinear finite element models, comparing their accuracy and efficiency across different problems using open source tools, and highlights the importance of problem-specific method selection.

Contribution

It provides a comprehensive benchmark of hyper-reduction methods for nonlinear finite element simulations, including open source implementation and analysis of their performance tradeoffs.

Findings

EQP generally outperforms interpolation methods in accuracy and efficiency

Performance depends on problem type and time integration method

Different methods are suitable for different nonlinear problems

Abstract

Hyper-reduction methods have gained increasing attention for their potential to accelerate reduced order models for nonlinear systems, yet their comparative accuracy and computational efficiency are not well understood. Motivated by this gap, we evaluate a range of hyper-reduction techniques for nonlinear finite element models across benchmark problems of varying complexity, assessing the inevitable tradeoff between accuracy and speedup. More specifically, we consider interpolation methods based on the gappy proper orthogonal decomposition as well as the empirical quadrature procedure (EQP), and apply them to the hyper-reduction of problems in nonlinear diffusion, nonlinear elasticity and Lagrangian hydrodynamics. Our numerical results are generated using the open source libROM, Laghos and MFEM numerical libraries. Our findings reveal that the comparative performance between hyper-reduction methods depends on both the problem and the choice of time integration method. The EQP method generally achieves lower relative errors than interpolation methods and is more efficient in terms of quadrature point usage, resulting in a lower wall time for the nonlinear diffusion and elasticity problems. However, its online computational cost is observed to be relatively high for Lagrangian hydrodynamics problems. Conversely, interpolation methods exhibit greater variability, especially with respect to the use of different time integration methods in the Lagrangian hydrodynamics problems. The presented results underscore the need for problem specific method selection to balance accuracy and efficiency, while also offering useful guidance for future comparisons and refinements of hyper-reduction techniques.