Local-global model reduction method for stochastic optimal control problems constrained by partial differential equations

TL;DR

This paper introduces a local-global model reduction technique combining reduced basis and GMsFEM to efficiently solve large-scale stochastic PDE-constrained optimal control problems, significantly reducing computational costs.

Contribution

The paper develops a novel local-global model reduction method that improves computational efficiency for stochastic PDE-constrained optimal control problems by integrating reduced basis and GMsFEM.

Findings

Achieves higher computational efficiency than local or global reduction alone

Enables fast online computation for multiple stochastic samples

Demonstrates effectiveness through numerical examples

Abstract

In this paper, a local-global model reduction method is presented to solve stochastic optimal control problems governed by partial differential equations (PDEs). If the optimal control problems involve uncertainty, we need to use a few random variables to parameterize the uncertainty. The stochastic optimal control problems require solving coupled optimality system for a large number of samples in the stochastic space to quantify the statistics of the system response and explore the uncertainty quantification. Thus the computation is prohibitively expensive. To overcome the difficulty, model reduction is necessary to significantly reduce the computation complexity. We exploit the advantages from both reduced basis method and Generalized Multiscale Finite Element Method (GMsFEM) and develop the local-global model reduction method for stochastic optimal control problems with PDE constraints. This local-global model reduction can achieve much more computation efficiency than using only local model reduction approach and only global model reduction approach. We recast the stochastic optimal problems in the framework of saddle-point problems and analyze the existence and uniqueness of the optimal solutions of the reduced model. In the local-global approach, most of computation steps are independent of each other. This is very desirable for scientific computation. Moreover, the online computation for each random sample is very fast via the proposed model reduction method. This allows us to compute the optimality system for a large number of samples. To demonstrate the performance of the local-global model reduction method, a few numerical examples are provided for different stochastic optimal control problems.