AI-Based Metamaterial Design

ACS Applied Materials & Interfaces, Год журнала: 2024, Номер 16(23), С. 29547 - 29569

Опубликована: Май 29, 2024

The use of metamaterials in various devices has revolutionized applications optics, healthcare, acoustics, and power systems. Advancements these fields demand novel or superior that can demonstrate targeted control electromagnetic, mechanical, thermal properties matter. Traditional design systems methods often require manual manipulations which is time-consuming resource intensive. integration artificial intelligence (AI) optimizing metamaterial be employed to explore variant disciplines address bottlenecks design. AI-based also enable the development by parameters cannot achieved using traditional methods. application AI leveraged accelerate analysis vast data sets as well better utilize limited via generative models. This review covers transformative impact for current challenges, emerging fields, future directions, within each domain are discussed.

Язык: Английский

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A data-driven inverse design framework for tunable phononic crystals

Engineering Structures, Год журнала: 2025, Номер 327, С. 119599 - 119599

Опубликована: Янв. 4, 2025

Язык: Английский

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Multiband elastic wave energy localization for highly amplified piezoelectric energy harvesting using trampoline metamaterials

Mechanical Systems and Signal Processing, Год журнала: 2023, Номер 200, С. 110593 - 110593

Опубликована: Июль 12, 2023

Язык: Английский

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Acoustic and mechanical metamaterials for energy harvesting and self-powered sensing applications

Materials Today Energy, Год журнала: 2023, Номер 37, С. 101387 - 101387

Опубликована: Авг. 20, 2023

Язык: Английский

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From defect mode to topological metamaterials: A state-of-the-art review of phononic crystals & acoustic metamaterials for energy harvesting

Sensors and Actuators A Physical, Год журнала: 2023, Номер 365, С. 114871 - 114871

Опубликована: Ноя. 27, 2023

Язык: Английский

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Inverse design of Bézier curve-based mechanical metamaterials with programmable negative thermal expansion and negative Poisson's ratio via a data augmented deep autoencoder

Materials Horizons, Год журнала: 2024, Номер 11(11), С. 2615 - 2627

Опубликована: Янв. 1, 2024

We introduce a novel deep learning-based inverse design framework with data augmentation for chiral mechanical metamaterials Bézier curve-shaped bi-material rib realizing wide range of negative thermal expansion and Poisson's ratio.

Язык: Английский

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Topological rainbow trapping and broadband piezoelectric energy harvesting of acoustic waves in gradient phononic crystals with coupled interfaces

Applied Acoustics, Год журнала: 2025, Номер 233, С. 110630 - 110630

Опубликована: Фев. 27, 2025

Язык: Английский

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Conformal Gradient-Index Phononic Crystal Lenses: Design, Theory, and Application on Non-planar Structures

Materials & Design, Год журнала: 2025, Номер unknown, С. 113854 - 113854

Опубликована: Март 1, 2025

Язык: Английский

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Beyond the limits of parametric design: Latent space exploration strategy enabling ultra-broadband acoustic metamaterials

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108595 - 108595

Опубликована: Май 15, 2024

Язык: Английский

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Machine learning-based optimization of segmented thermoelectric power generators using temperature-dependent performance properties

Applied Energy, Год журнала: 2023, Номер 355, С. 122216 - 122216

Опубликована: Ноя. 18, 2023

Segmented thermoelectric generators (STEGs) provide an excellent platform for thermal energy harvesting devices because they improve power generation performance across a broad range of operating temperatures. Despite the benefit direct energy-to-electricity conversion, conventional STEG optimization approaches are unable to systematic method selecting optimal multiple stacks p- and-n-type materials (TEs) legs from set numerous TE materials. In this study, we propose based on machine learning find maximization. A deep neural network (DNN) is trained using initial dataset generated via Finite Element Method (FEM), with inputs including temperature-dependent properties and n-type materials, lengths each segment, external loads, as well corresponding outputs. The DNN captures inherent nonlinear relationship between these combination genetic algorithm (GA) efficiently navigates vast design space 88 p-type 70 along device factors. It formulates four stacked segment pairs in n-leg TEGs, targeting new superior designs enhanced power, efficiency, or both. iteratively refined active (AL) by incorporating enhance prediction accuracy. optimized STEGs exhibit efficiency that 1.91 1.5 times higher, respectively, than top training composed 157.916 STEGs. Furthermore, compared TEG without segmentation, our discovered high-performing designs.

Язык: Английский

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