Accelerated construction of projection-based reduced-order models via incremental approaches
Abstract
We present an accelerated greedy strategy for training of projection-based reduced-order models for parametric steady and unsteady partial differential equations. Our approach exploits hierarchical approximate proper orthogonal decomposition to speed up the construction of the empirical test space for least-square Petrov-Galerkin formulations, a progressive construction of the empirical quadrature rule based on a warm start of the non-negative least-square algorithm, and a two-fidelity sampling strategy to reduce the number of expensive greedy iterations. We illustrate the performance of our method for two test cases: a two-dimensional compressible inviscid flow past a LS89 blade at moderate Mach number, and a three-dimensional nonlinear mechanics problem to predict the long-time structural response of the standard section of a nuclear containment building under external loading.
Cite
@article{arxiv.2401.07108,
title = {Accelerated construction of projection-based reduced-order models via incremental approaches},
author = {Eki Agouzal and Tommaso Taddei},
journal= {arXiv preprint arXiv:2401.07108},
year = {2024}
}