Minimizing Structural Vibrations via Guided Flow Matching Design Optimization

TL;DR

This paper introduces a novel guided flow matching-based optimization method for designing plate-like structures with beadings to minimize vibrations, outperforming traditional approaches in generating manufacturable, low-vibration designs.

Contribution

The work presents a new design optimization framework combining generative flow matching and surrogate modeling to explore structural designs without manual parameter tuning.

Findings

Generated designs show significantly reduced vibrations.

Method outperforms random search, heuristics, and genetic optimization.

Designs are diverse and manufacturable.

Abstract

Structural vibrations are a source of unwanted noise in engineering systems like cars, trains or airplanes. Minimizing these vibrations is crucial for improving passenger comfort. This work presents a novel design optimization approach based on guided flow matching for reducing vibrations by placing beadings (indentations) in plate-like structures. Our method integrates a generative flow matching model and a surrogate model trained to predict structural vibrations. During the generation process, the flow matching model pushes towards manufacturability while the surrogate model pushes to low-vibration solutions. The flow matching model and its training data implicitly define the design space, enabling a broader exploration of potential solutions as no optimization of manually-defined design parameters is required. We apply our method to a range of differentiable optimization objectives, including direct optimization of specific eigenfrequencies through careful construction of the objective function. Results demonstrate that our method generates diverse and manufacturable plate designs with reduced structural vibrations compared to designs from random search, a criterion-based design heuristic and genetic optimization. The code and data are available from https://github.com/ecker-lab/Optimizing_Vibrating_Plates.