Design of one-dimensional acoustic metamaterials using machine learning and cell concatenation
Journal
Structural and Multidisciplinary Optimization
Journal Volume
63
Journal Issue
5
Pages
2399-2423
Date Issued
2021
Author(s)
Abstract
Metamaterial systems have opened new, unexpected, and exciting paths for the design of acoustic devices that only few years ago were considered completely out of reach. However, the development of an efficient design methodology still remains challenging due to highly intensive search in the design space required by the conventional optimization-based approaches. To address this issue, this study develops two machine learning (ML)-based approaches for the design of one-dimensional periodic and non-periodic metamaterial systems. For periodic metamaterials, a reinforcement learning (RL)-based approach is proposed to design a metamaterial that can achieve user-defined frequency band gaps. This RL-based approach surpasses conventional optimization-based methods in the reduction of computation cost when a near-optimal solution is acceptable. Leveraging the capability of exploration in RL, the proposed approach does not require any training datasets generation and therefore can be deployed for online metamaterial design. For non-periodic metamaterials, a neural network (NN)-based approach capable of learning the behavior of individual material units is presented. By assembling the NN representation of individual material units, a surrogate model of the whole metamaterial is employed to determine the properties of the resulting assembly. Interestingly, the proposed approach is capable of modeling different metamaterial assemblies satisfying user-defined properties while requiring only a one-time network training procedure. Also, the NN-based approach does not need a pre-defined number of material unit cells, and it works when the physical model of the unit cell is not well understood, or the situation where only the sensor measurements of the unit cell are available. The robustness of the proposed two approaches is validated through numerical simulations and design examples. ? 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
Subjects
Acoustic metamaterial
Machine learning
Neural network
Phononic crystal
Reinforcement learning
Cells
Cytology
Design
Energy gap
Metamaterials
Acoustic metamaterials
Conventional optimization
Near-optimal solutions
Neural network (nn)
NN-based approach
Sensor measurements
Training data sets
User-defined frequency
Learning systems
Type
journal article