Block-structured Operator Inference for coupled multiphysics model reduction

TL;DR

This paper introduces a block-structured Operator Inference method for multiphysics model reduction, improving computational efficiency while preserving physical properties and accuracy in coupled aeroelastic systems.

Contribution

It develops a structured approach to learn reduced-order models that respect physics coupling, enhancing efficiency and stability over traditional monolithic methods.

Findings

20% average online prediction speedup

Preserves stability and accuracy

Effective for aeroelastic systems

Abstract

This paper presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the governing equation structure for each physics component and the structure of the coupling terms. Once the multiphysics structure is specified, the reduced-order model is learned from snapshot data following the nonintrusive Operator Inference methodology. In addition to preserving physical system structure, which in turn permits preservation of system properties such as stability and second-order structure, the block-structured approach has the advantages of reducing the overall dimensionality of the learning problem and admitting tailored regularization for each physics component. The numerical advantages of the block-structured formulation over a monolithic Operator Inference formulation are demonstrated for aeroelastic analysis, which couples aerodynamic and structural models. For the benchmark test case of the AGARD 445.6 wing, block-structured Operator Inference provides an average 20% online prediction speedup over monolithic Operator Inference across subsonic and supersonic flow conditions in both the stable and fluttering parameter regimes while preserving the accuracy achieved with monolithic Operator Inference.