DeepF-fNet: a physics-informed neural network for vibration isolation optimization

TL;DR

DeepF-fNet is a physics-informed neural network framework that efficiently optimizes vibration isolation in structures, outperforming traditional algorithms in speed while maintaining accuracy, suitable for real-time applications.

Contribution

This paper introduces DeepF-fNet, a novel physics-informed neural network that effectively solves nonlinear inverse eigenvalue problems for vibration optimization in real-time.

Findings

DeepF-fNet achieves faster computation than genetic algorithms.

It provides comparable vibration suppression results.

Validated on a locally resonant metamaterial case study.

Abstract

Structural optimization is essential for designing safe, efficient, and durable components with minimal material usage. Traditional methods for vibration control often rely on active systems to mitigate unpredictable vibrations, which may lead to resonance and potential structural failure. However, these methods face significant challenges when addressing the nonlinear inverse eigenvalue problems required for optimizing structures subjected to a wide range of frequencies. As a result, no existing approach has effectively addressed the need for real-time vibration suppression within this context, particularly in high-performance environments such as automotive noise, vibration and harshness, where computational efficiency is crucial. This study introduces DeepF-fNet, a novel neural network framework designed to replace traditional active systems in vibration-based structural optimization. Leveraging DeepONets within the context of physics-informed neural networks, DeepF-fNet integrates both data and the governing physical laws. This enables rapid identification of optimal parameters to suppress critical vibrations at specific frequencies, offering a more efficient and real-time alternative to conventional methods. The proposed framework is validated through a case study involving a locally resonant metamaterial used to isolate structures from user-defined frequency ranges. The results demonstrate that DeepF-fNet outperforms traditional genetic algorithms in terms of computational speed while achieving comparable results, making it a promising tool for vibration-sensitive applications. By replacing active systems with machine learning techniques, DeepF-fNet paves the way for more efficient and cost-effective structural optimization in real-world scenarios.