Reinforcement learning applied to metamaterial design

Shah, T.; Zhuo, Linwei; Lai, Peter; Rosa-Moreno, Amaris De La; Amirkulova, Feruza; Gerstoft, Peter

doi:10.1121/10.0005545

Cited by 32 publications

(15 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many intelligent optimization algorithms had been chosen in optimizing the structural parameters of acoustic metamaterial, such as the level set–based topology optimization method utilized by Noguchi et al [ 22 ], genetic algorithm used by Li et al [ 23 ], reinforcement learning applied by Shah et al [ 24 ], and topology optimization adopted by Dong et al [ 12 , 25 ]. In this study, the particle swarm optimization algorithm [ 26 ] with the given initial values is used to optimize structural parameters of the investigated metamaterial cell.…”

Section: Optimization Of the Structural Parametersmentioning

confidence: 99%

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

et al. 2022

View full text Add to dashboard Cite

An acoustic metamaterial absorber of parallel–connection square Helmholtz resonators is proposed in this study, and its sound absorption coefficients are optimized to reduce the noise for the given conditions in the factory. A two–dimensional equivalent simulation model is built to obtain the initial value of parameters and a three–dimensional finite element model is constructed to simulate the sound absorption performance of the metamaterial cell, which aims to improve the research efficiency. The optimal parameters of metamaterial cells are obtained through the particle swarm optimization algorithm, and its effectiveness and accuracy are validated through preparing the experimental sample using 3D printing and measuring the sound absorption coefficient by the standing wave tube detection. The consistency between the experimental data and simulation data verifies feasibility of the proposed optimization method and usefulness of the developed acoustic metamaterial absorber, and the desired sound absorption performances for given conditions are achieved. The experimental results prove that parallel–connection square Helmholtz resonators can achieve an adjustable frequency spectrum for the low frequency noise control by parameter optimization, which is propitious to promote its application in reducing the noise in the factory.

show abstract

Section: Optimization Of the Structural Parametersmentioning

confidence: 99%

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The proposed method can be extended to design 3D lenses including air-born sound and underwater acoustic, elastodynamic, or electromagnetic wave localization and focusing effects. The gradient assisted inverse design of acoustic lens can be further enhanced and implemented by integrating the gradient information, i.e., Equation (37) with deep reinforcement learning algorithms [45] and generative networks [46] which have potential to search for the globally optimized devices over a broad range of parameters and can provide better solutions than ones produced by the-state-of-the-art optimization algorithms.…”

Section: Discussionmentioning

confidence: 99%

Sound Localization through Multi-Scattering and Gradient-Based Optimization

2021

Self Cite

View full text Add to dashboard Cite

A gradient-based optimization (GBO) method is presented for acoustic lens design and sound localization. GBO uses a semi-analytical optimization combined with the principle of acoustic reciprocity. The idea differs from earlier inverse designs that use topology optimization tools and generic algorithms. We first derive a formula for the gradients of the pressure at the focal point with respect to positions of a set of cylindrical scatterers. The analytic form of the gradients enhances modeling capability when combined with optimization algorithms and parallel computing. The GBO algorithm maximizes the sound amplification at the focal point and enhances the sound localization by evaluating pressure derivatives with respect to the cylinder positions and then perturbatively optimizing the position of each cylinder in the lens while incorporating multiple scattering between the cylindrical scatterers. The results of the GBO of the uni- and multi-directional broadband acoustic lens designs are presented including several performance measures for the frequency dependence and the incidence angle. A multi-directional broadband acoustic lens is designed to localize the sound and to focus acoustic incident waves received from multiple directions onto a predetermined localization region or focal point. The method is illustrated for configurations of sound hard and sound soft cylinders as well as clusters of elastic thin shells in water.

show abstract

“…A more detailed description of multiple scattering problem formulation can be found in Refs. [7,22,37,39]. Our goal in this study is to find an efficient way to obtain scatterer locations that minimize σ at certain wavenumbers (inverse design), and hence, pave the way for the creation of acoustic cloaks.…”

Section: Multiple Scattering Theorymentioning

confidence: 99%

“…The last decade has witnessed a surge of scientific publications in which deep learning, reinforcement learning and generative modelling were applied in different areas of science and engineering [17][18][19][20]. Recent advances in the field of machine learning have enabled a new, data driven, approach with a great promise to solve problems such as the inverse design in acoustic metamaterials [21][22][23][24][25]. Early machine learning applications in acoustic forward design date back to the late 90s when Jenison first used spherical basis function of fully-connected neural networks (FC) for approximating the acoustic scattering of a rigid scatterer [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…Meng et al [33] investigated the inverse acoustic scattering problem that reconstructs the obstacle shape with far-field information using fully connected neural network (FC). Shah et al [22] employed deep reinforcement learning algorithms to design acoustic cloaks by adjusting positions and radii of cylindrical structures; these reinforcement learning models are capable of predicting better results than the-state-of-the-art gradient based optimization algorithms. Wu et al [23] proposed machine learning framework for the design of one-dimensional periodic and non-periodic acoustic metamaterials using deep learning and reinforcement learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Acoustic Cloak Design via Machine Learning

Tran,

Amirkulova,

Khatami

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Acoustic metamaterials are engineered microstructures with special mechanical and acoustic properties enabling exotic effects such as wave steering, focusing and cloaking. The design of acoustic cloaks using scattering cancellation has traditionally involved the optimization of metamaterial structure based on direct computer simulations of the total scattering cross section (TSCS) for a large number of configurations. Here, we work with sets of cylindrical objects confined in a region of space and use machine learning methods to streamline the design of 2D configurations of scatterers with minimal TSCS demonstrating cloaking effect at discrete sets of wavenumbers. After establishing that artificial neural networks are capable of learning the TSCS based on the location of cylinders, we develop an inverse design algorithm, combining variational autoencoders and the Gaussian process, for predicting optimal arrangements of scatterers given the TSCS. We show results for up to eight cylinders and discuss the efficiency and other advantages of the machine learning approach.

show abstract

Reinforcement learning applied to metamaterial design

Cited by 32 publications

References 55 publications

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

Development and Optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel–Connection Square Helmholtz Resonators

Sound Localization through Multi-Scattering and Gradient-Based Optimization

Acoustic Cloak Design via Machine Learning

Contact Info

Product

Resources

About