Adaptive space-time model order reduction with dual-weighted residual (MORe DWR) error control for poroelasticity

TL;DR

This paper develops an adaptive space-time model order reduction method with error control for poroelasticity problems, significantly reducing computational effort while accurately estimating quantities of interest.

Contribution

It introduces a goal-oriented adaptive POD-based reduced-order model with dual-weighted residual error estimation for Biot systems in poroelasticity.

Findings

Reduces number of full-order-model solves

Provides robust error estimation for quantities of interest

Demonstrates efficiency on benchmark problems

Abstract

In this work, the space-time MORe DWR (Model Order Reduction with Dual-Weighted Residual error estimates) framework is extended and further developed for single-phase flow problems in porous media. Specifically, our problem statement is the Biot system which consists of vector-valued displacements (geomechanics) coupled to a Darcy flow pressure equation. The MORe DWR method introduces a goal-oriented adaptive incremental proper orthogonal decomposition (POD) based-reduced-order model (ROM). The error in the reduced goal functional is estimated during the simulation, and the POD basis is enriched on-the-fly if the estimate exceeds a given threshold. This results in a reduction of the total number of full-order-model solves for the simulation of the porous medium, a robust estimation of the quantity of interest and well-suited reduced bases for the problem at hand. We apply a space-time Galerkin discretization with Taylor-Hood elements in space and a discontinuous Galerkin method with piecewise constant functions in time. The latter is well-known to be similar to the backward Euler scheme. We demonstrate the efficiency of our method on the well-known two-dimensional Mandel benchmark and a three-dimensional footing problem.