Model Reduction of Parametric Differential-Algebraic Systems by Balanced Truncation

TL;DR

This paper presents a novel method combining balanced truncation with reduced basis techniques to efficiently reduce parametric differential-algebraic systems, supported by error estimation and applied to fluid dynamics and mechanics models.

Contribution

It introduces a new approach for model reduction of parametric differential-algebraic systems using balanced truncation combined with reduced basis methods and residual-based error estimators.

Findings

Efficient reduction of complex systems demonstrated on fluid dynamics models.

Significant computational savings achieved compared to naive approaches.

Validated accuracy through residual-based error estimation.

Abstract

We deduce a procedure to apply balanced truncation to parameter-dependent differential-algebraic systems. For that we solve multiple projected Lyapunov equations for different parameter values to compute the Gramians that are required for the truncation procedure. As this process would lead to high computational costs if we perform it for a large number of parameters, we combine this approach with the reduced basis method that determines a reduced representation of the Lyapunov equation solutions for the parameters of interest. Residual-based error estimators are then used to evaluate the quality of the approximations. After introducing the procedure for a general class of differential-algebraic systems we turn our focus to systems with a specific structure, for which the method can be applied particularly efficiently. We illustrate the efficiency of our approach on several models from fluid dynamics and mechanics.