A variational multiscale approach to PDE-constrained optimization problems arising in Data-Driven Computational Mechanics

TL;DR

This paper develops a stable finite element framework using the Variational MultiScale approach for PDE-constrained optimization in Data-Driven Computational Mechanics, focusing on reaction-diffusion problems.

Contribution

It introduces a well-posed, stable finite element approximation for primal and dual PDE-constrained optimization problems using the Variational MultiScale method, with analysis of sub-grid scale choices.

Findings

Stable and consistent finite element schemes are established.

Well-posedness is proven for different sub-grid scale models.

Numerical tests demonstrate the effectiveness of the proposed methods.

Abstract

We consider the primal and dual forms of the optimality conditions for PDE-contrained optimization problems arising in Data-Driven Computational Mechanics when specialized to the reaction-diffusion context. Starting with the continuous setting, we establish well-posedness of such concomitant formulations. Then, we propose stable and consistent finite element approximations for these underlying primal and dual problems relying on the Variational MultiScale framework. For quasi-uniform finite element partitions, we investigate approximations' general properties and establish well-posedness for two canonical choices of the sub-grid scales, i.e., the Algebraic Sub-Grid Scale and Orthogonal Sub-Grid Scale. Moreover, for continuous finite element functions, we are able to move back and forth between the discrete primal and dual formulations only by changing the design of the stabilization parameters. To conclude, we stress-test the proposed approximations through a series of progressively sophisticated cases, providing both a comparative and qualitative assessment of their numerical performance.