Query: deep learning metamaterial

title: Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning
abstract: The introduction of "metamaterials" has had a profound impact on several fields, including electromagnetics. Designing a metamaterial's structure on demand, however, is still an extremely time consuming process. As an efficient machine learning method, deep learning has been widely used for data classification and regression in recent years and in fact shown good generalization performance. We have built a deep neural network for on demand design. With the required reflectance as input, the parameters of the structure are automatically calculated and then output to achieve the purpose of designing on demand. Our network has achieved low mean square errors (MSE) with MSE of 0.005 on both the training and test sets. The results indicate that using deep learning to train the data, the trained model can more accurately guide the design of the structure, thereby speeding up the design process. Compared with the traditional design process, using deep learning to guide the design of metamaterials can achieve faster, more accurate, and more convenient purposes.

title: Customized Inverse Design of Metamaterial Absorber Based on Target Driven Deep Learning Method
abstract: Metamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full wave numerical simulations and trial and error methods. It is time consuming and laborious to obtain the optimal design parameters. Recently, extensive researches have shown advantages and superiority of the deep learning method in solving non intuitive problem. Several attempts have been made to demonstrate Artificial Intelligence (AI) usage in the electromagnetic field. In this article, a target driven method empowered by deep learning to realize customized metamaterial absorber (MMA) design has been proposed and demonstrated numerically. Unlike previous deep learning based design methods for MMs which directly use the spectrum response to generate the MM's design parameters, this method takes the frequency domain response of the absorber as the intermediary bridge to establish the mapping between MMAs geometry/material parameters and customized figure of merits. The proposed design framework greatly simplified the design process of MMAs, and it can also be generalized to realize automatic inverse design for other kinds of metasurfaces.

title: Accelerating Auxetic Metamaterial Design with Deep Learning
abstract: Most monolithic engineering materials have positive Poisson's ratio and contract laterally relative to the direction of imparted strain. However, auxetics are a class of materials that expand laterally to the direction in which they are strained, exhibiting negative Poisson's ratio (NPR) The key to an auxetic material's NPR is the internal structural mechanism that creates a rotational or leveraging effect of unit cell struts to the surrounding cells causing counterintuitive expansion. Combining an array of auxetic cells forms metamaterials with tunable gradient properties depending on the unit cell geometry used and concentrations of the respective Poisson's ratio values. Many unit cell architectures have been studied; however, significant focus has been conducted on the variations of the reentrant honeycomb cell which behaves within a range of strain, as an effective NPR unit cell expanding positively in all directions with positive imparted strain. Reentrant honeycomb cells are the focus of this study because of their large basis of research and relative geometric simplicity for tuning behavior. Due to the unique mechanical properties of auxetics, NPR materials are used in engineering applications, especially when thematerials undergo significant deformation. However, the behavior of auxetics in soft bodied materials is still being developed and makes their interaction in applications difficult to predict. Applications of auxetic metamaterials have been explored in previous literature, ranging from impact lattice structures, medical stents, and others described in depth in previous review studies, whose main function is derived from their behavior under strain. Of particular importance to our work is the application of auxetic metamaterials in 2D and 3D soft bodies due to this counterintuitive deformation behavior. 2D elastomeric lattices are currently in use and being explored in sportswear and biomedical films that are able to conform to the body. However, out of plane strain is still a design fault, impeding progress for both applications. 3D application studies have also been conducted on the use of biomimetic soft body manipulators using fluidic control. Manipulator applications utilize conventional kinematic models for simple movements but such models restrain researchers to a predefined control methodology when creating more complex control systems. Researchers have explored auxetic geometries to create a soft bodied cylinder using auxetic cell buckling failure for angular actuation. This work demonstrated fundamental aspects of NPR in 3D actuation and incentivized our study to develop a workflow for designing dynamic auxetics, using a similar form of an angularly actuated cylinder. Aside from the pressure driven actuation control system, in many of these cases, the only way for the intricate structures to bemanufactured is through detailed casting or, as we use in our approach, via an additive manufacturing system. Additive manufacturing is an ideal method of creating various types of metamaterials because 3D printers are capable of fabricating complex architectures. The coupled ability to rapidly and iteratively discover new designs with machine learning and experimentally validate results with additive manufacturing marks a novel discovery process in manufacturing and materials characterization. The programmability of the additive J. K. Wilt, C. Yang, Prof. G. X. Gu Department of Mechanical Engineering University of California, Berkeley 6177 Etcheverry Hall, Berkeley, CA 94720 1740, USA E mail: ggu@berkeley.edu

title: Accelerating Auxetic Metamaterial Design with Deep Learning

title: Terahertz Metamaterial Intelligent Identification by Private Preserving Deep Learning
abstract: , designed with special structures, and can exhibit controllable electromagnetic properties. To address the looming threat of stealthy monitoring caused by metamaterials, the detection and identification of them is the next research frontier but have not yet been explored. In this research, a crypto oriented convolutional neural network is designed to identify metamaterial in mixtures, which is an accurate and secure paradigm of AI based terahertz smart sensing.

title: Secure Deep Learning for Intelligent Terahertz Metamaterial Identification
abstract: Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5 fold cross validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence based material identification.

title: Inverse engineering of electromagnetically induced transparency in terahertz metamaterial via deep learning
abstract: In this paper, we apply the deep learning network to the inverse engineering of electromagnetically induced transparency (EIT) in terahertz metamaterial. We take three specific points of the EIT spectrum with six inputs (each specific point has two physical values with frequency and amplitude) into the deep learning model to predict and inversely design the geometrical parameters of EIT metamaterials. We propose this algorithm for the general inverse design of EIT metamaterials, and we demonstrate that our method is functional by taking one example structure. Our deep learning model exhibits a mean square error of 0.0085 in the training set and 0.014 in the test set. We believe that this finding will open a new approach for designing geometrical parameters of EIT metamaterials, and it has great potential to enlarge the applications of the THz EIT metamaterial.

title: Deep Generative Modeling for Mechanistic based Learning and Design of Metamaterial Systems
abstract: Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inverse design of metamaterial and its multiscale system is challenging due to high dimensional topological design space, multiple local optima, and high computational cost. To address these hurdles, we propose a novel data driven metamaterial design framework based on deep generative modeling. A variational autoencoder (VAE) and a regressor for property prediction are simultaneously trained on a large metamaterial database to map complex microstructures into a low dimensional, continuous, and organized latent space. We show in this study that the latent space of VAE provides a distance metric to measure shape similarity, enable interpolation between microstructures and encode meaningful patterns of variation in geometries and properties. Based on these insights, systematic data driven methods are proposed for the design of microstructure, graded family, and multiscale system. For microstructure design, the tuning of mechanical properties and complex manipulations of microstructures are easily achieved by simple vector operations in the latent space. The vector operation is further extended to generate metamaterial families with a controlled gradation of mechanical properties by searching on a constructed graph model. For multiscale metamaterial systems design, a diverse set of microstructures can be rapidly generated using VAE for target properties at different locations and then assembled by an efficient graph based optimization method to ensure compatibility between adjacent microstructures. We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems that achieve desired distortion behaviors.

title: Deep learning for design metamaterial electromagnetic induction transparent device
abstract: In this paper, we propose a deep learning model that can be used to reverse design metamaterial electromagnetic induction transparent (EIT) devices. This is a problem that is difficult to achieve with traditional numerical calculation methods. We use the coordinates of six specific points on the EIT transmission spectrum as the input of the neural network, and then the network can predict the structural parameters of the corresponding EIT device. We cite an example to prove that our method can be used to efficiently reverse design the structure of EIT devices. We believe that this method will open up a new way for the structural design of EIT devices and has great potential for expanding the application of terahertz EIT metamaterials.

title: Deep learning for topology optimization of 2D metamaterials
abstract: Data driven models are rising as an auspicious method for the geometrical design of materials and structural systems. Nevertheless, existing data driven models customarily address the optimization of structural designs rather than metamaterial designs. Metamaterials are emerging as promising materials exhibiting tailorable and unprecedented properties for a wide spectrum of applications. In this paper, we develop a deep learning (DL) model based on a convolutional neural network (CNN) that predicts optimal metamaterial designs. The developed DL model non iteratively optimizes metamaterials for either maximizing the bulk modulus, maximizing the shear modulus, or minimizing the Poisson's ratio (including negative values) The data are generated by solving a large set of inverse homogenization boundary values problems, with randomly generated geometrical features from a specific distribution. Such s data driven model can play a vital role in accelerating more computationally expensive design problems, such as multiscale metamaterial systems.