Design of thermal cloaks with isotropic materials based on machine learning

Qi, Yunchao; Liu, Chenwei; Meng, Sheng; Liang, Jun; Kadic, Muamer; Fang, Guodong

doi:10.1016/j.ijheatmasstransfer.2022.122716

Cited by 21 publications

(11 citation statements)

References 36 publications

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“…Based on the reviewed literature (Figure 8), most MLaccelerated design optimization works were based on this approach, among which the most commonly used ML models were convolutional neural networks (CNN) [37,45,[204][205][206][207][208] and multilayer perceptron (MLP). [40,159,[209][210][211][212] Typically, CNNs were used for pixelated design representations with high geometric freedom or design dimensionality (allowing more complex designs), while MLPs were used for parametric or shape designs with lower design dimensionality. These two models were mostly used when the training data size was larger than 700.…”

Section: Accelerated Optimization Via Data-driven Property Predictionmentioning

confidence: 99%

Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Lee,

Chen,

Wang

et al. 2023

Advanced Materials

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Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next‐generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure–property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data‐driven design. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data‐driven modules, encompassing data acquisition, machine learning‐based unit cell design, and data‐driven multiscale optimization. The approaches are further categorized within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.

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Section: Accelerated Optimization Via Data-driven Property Predictionmentioning

confidence: 99%

Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Lee,

Chen,

Wang

et al. 2023

Advanced Materials

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“…Although these metamaterials have been used to design advanced self-adaptive optical cloaks in wave systems, [53] they have failed in diffusion systems like heat transfer due to the lack of controllable degrees of freedom. Existing machine learning-based thermal diffusion metamaterials are dictated by the inverse design methods, [54][55][56][57] which calculate the parameters of materials and sizes for desired functions. In addition, once these metamaterials are prepared, their functions are not switchable, lacking the ability to adapt to various scenes.…”

Section: Doi: 101002/adma202305791mentioning

confidence: 99%

Deep Learning‐Assisted Active Metamaterials with Heat‐Enhanced Thermal Transport

Jin,

Xu,

et al. 2023

Advanced Materials

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Heat management is crucial for state‐of‐the‐art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working‐temperature ranges, and the need for manual intervention, which remain long‐term and tricky obstacles for the most advanced self‐adaptive metamaterials. To surmount these barriers, we introduce heat‐enhanced thermal diffusion metamaterials powered by deep learning. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, we design two metadevices with on‐demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments.This article is protected by copyright. All rights reserved

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Section: Introductionmentioning

confidence: 99%

“…The most recent works have made pioneering strides in designing thermal metamaterials via DL techniques, achieving the successful creation of functional thermal metadevices with thermal concentrating and cloaking. [38,39] Further explorations about design of thermal metamaterials need to be conducted to meet wider design requirements, such as automation, real-time, and customizability. To achieve the intelligent design of thermal metamaterials, we propose an automatic, real-time, and customizable design method via a pre-trained deep generative model of fullparameter thermal conductivity tensors (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Deep‐Learning‐Enabled Intelligent Design of Thermal Metamaterials

et al. 2023

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Thermal metamaterials are mixture‐based materials that are engineered to manipulate, control, and process the flow of heat, enabling numerous advanced thermal metadevices. Conventional thermal metamaterials are predominantly designed with tractable regular geometries owing to the delicate analytical solution and easy‐to‐implement effective structures. Nevertheless, it is challenging to achieve the design of thermal metamaterials with arbitrary geometry, letting alone intelligent (automatic, real‐time, and customizable) design of thermal metamaterials. Here, an intelligent design framework of thermal metamaterials is presented via a pre‐trained deep learning model, which gracefully achieves the desired functional structures of thermal metamaterials with exceptional speed and efficiency, regardless of arbitrary geometry. It possesses incomparable versatility and is of great flexibility to achieve the corresponding design of thermal metamaterials with different background materials, anisotropic geometries, and thermal functionalities. The transformation thermotics‐induced, freeform, background‐independent, and omnidirectional thermal cloaks, whose structural configurations are automatically designed in real‐time according to shape and background, are numerically and experimentally demonstrated. This study sets up a novel paradigm for an automatic and real‐time design of thermal metamaterials in a new design scenario. More generally, it may open a door to the realization of an intelligent design of metamaterials in also other physical domains.

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Design of thermal cloaks with isotropic materials based on machine learning

Cited by 21 publications

References 36 publications

Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Deep Learning‐Assisted Active Metamaterials with Heat‐Enhanced Thermal Transport

Deep‐Learning‐Enabled Intelligent Design of Thermal Metamaterials

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