Dispersion relation prediction and structure inverse design of elastic metamaterials via deep learning

Jiang, Weifeng; Zhu, Yangyang; Yin, Guofu; Lu, Houhong; Xie, Luofeng; Yin, Ming

doi:10.1016/j.mtphys.2022.100616

Cited by 21 publications

(12 citation statements)

References 44 publications

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“…Table 4 summarizes the existing physics-informed AI and AI inverse design models with respect to the algorithm, advantage and disadvantage of the theoretical analysis, dielectric material selection, and structural design. [11,12,41,42,[81][82][83]90,[116][117][118][119][120]…”

Section: Structural Design and Optimization For Contact Interfacesmentioning

confidence: 99%

“…Existing physics-informed AI and AI inverse design models with respect to the algorithm, advantage and disadvantage on the theoretical analysis, dielectric material selection, and structural design. [11,12,41,42,[81][82][83]90,[116][117][118][119][120] Physics-informed AI models AI inverse design models Algorithm Advantage Disadvantage Algorithm Advantage Disadvantage Theoretical analysis…”

Section: Strategic Inverse Design Toward Integrated Teng Devicesmentioning

confidence: 99%

“…• Physics-informed genetic programming (GP) [ 117,118] • Suitable for complex optimization • GWO inverse design (GWOID) [ 11] • High accuracy • Physics-informed ML [119] • • DL inverse design [ 90] • Well learning • Physics-informed DL [81] • Great learning • Recurrent NN inverse design [ 41] • Appropriate for processing sequence data • Physics-informed ML [82] • Self-upgrades without new code or algorithm • DL inverse design [ 120] • Well learning • Physics-informed DL [83] • Well learning • DNN inverse design [ 12] • Extract richer [123], 2023, Wiley) [123] c) Inverse design paradigms of TENGs from characterizing the key physical information, identifying the performance requirements, integrating and developing physics-informed AI models with limited amount of data, to using the AI-based material and structural designs to improve the fabrication and integration. ) key achievements and appearance of TENGs at the six levels.…”

Section: Strategic Inverse Design Toward Integrated Teng Devicesmentioning

confidence: 99%

See 2 more Smart Citations

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

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Section: Structural Design and Optimization For Contact Interfacesmentioning

confidence: 99%

Section: Strategic Inverse Design Toward Integrated Teng Devicesmentioning

confidence: 99%

Section: Strategic Inverse Design Toward Integrated Teng Devicesmentioning

confidence: 99%

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Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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“…Meta-structure optimisation methods have previously employed finite element analysis (FEA) as a basis for structure-property enhancements [16,17]. These include non-linear programming [18], gradient-descent [19,20,21], Bayesian optimisation [22,23], deep learning [24,25] and various evolutionary algorithms [26,27,28,29,30] as a basis for the optimisation frameworks. These optimisation frameworks rely on topology [31,3,17,25] and parametric design approaches [22,26,32,33,34,5] to alter the arrangement of metamaterial lattices [23,4], chiral structures [34,32] and thin-walled cellular solids [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Compressive properties of parametrically optimised mechanical metamaterials based on 3D projections of 4D geometries

Cerniauskas¹,

Alam²

2023

Preprint

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The design process of 3D mechanical metamaterials is still an emerging field and in this paper, we propose for the first time, a new design and optimisation approach based on 3D projections of 4D geometries (4-polytopes) and evolutionary algorithms. We find that through iterative parametric optimisation, 4-polytope projected mechanical metamaterials can be optimised to achieve both high specific stiffness and high specific yield strengths. Samples manufactured using a low-stereolithography method were tested in compression. We find that optimised tesseracts (8-cell structures) had a higher specific yield strength (22.8 kNm/kg) than that of honeycomb structures tested out-ofplane (19.4 kNm/kg) and a specific stiffness of (0.68 MNm/kg) which is more than 3-fold that of gyroid structures. The compressive strength to solid-modulus ratio of the 8-cell tesseract is very high (3×10 −3 ), exceeding that of out-ofplane honeycombs, which are themselves closer in value to 5-cell pentatopes (2×10 −3 ). 8-cell and 5-cell structures are in the region of one order of magnitude higher than 16-cell and 24-cell structures (∼ 2 × 10 −4 − 8 × 10 −4 ) and are hence comparable to nanostructured metamaterials. The 8-cell tesseracts are 18% stiffer, 43% stronger, and 19% tougher in compression than out-of-plane honeycomb structures, but unlike honeycombs, 8-cell tesseracts are 3D structures with cubic symmetry. Architecture has a profound effect on the relative consistency of properties with cubically symmetric structures displaying the greatest levels of consistency in terms of both strength and stiffness reduction as a function of pore space. The results presented in this paper showcase the potential of this new class of mechanical metamaterial based on 3D projected 4-polytopes.

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“…As a result, PnCs with anticipated band gaps can be generated using the DL model. Jiang et al established the mapping between the full diagram of real dispersion relation curves and the structural topology of in-plane PnCs via the conditional GAN, based on which the dispersion relation can be proactively tailored within diverse configurations [ 43 ]. Focusing on the transmission behavior of acoustic metamaterial, the conditional GAN is applied to generate the cell candidate relating to desired transmission loss of plane waves [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

Inverse Design of Micro Phononic Beams Incorporating Size Effects via Tandem Neural Network

Miao

et al. 2023

Materials

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Phononic crystals of the smaller scale show a promising future in the field of vibration and sound reduction owing to their capability of accurate manipulation of elastic waves arising from size-dependent band gaps. However, manipulating band gaps is still a major challenge for existing design approaches. In order to obtain the microcomposites with desired band gaps, a data drive approach is proposed in this study. A tandem neural network is trained to establish the mapping relation between the flexural wave band gaps and the microphononic beams. The dynamic characteristics of wave motion are described using the modified coupled stress theory, and the transfer matrix method is employed to obtain the band gaps within the size effects. The results show that the proposed network enables feasible generated micro phononic beams and works better than the neural network that outputs design parameters without the help of the forward path. Moreover, even size effects are diminished with increasing unit cell length, the trained model can still generate phononic beams with anticipated band gaps. The present work can definitely pave the way to pursue new breakthroughs in micro phononic crystals and metamaterials research.

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Dispersion relation prediction and structure inverse design of elastic metamaterials via deep learning

Cited by 21 publications

References 44 publications

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Compressive properties of parametrically optimised mechanical metamaterials based on 3D projections of 4D geometries

Inverse Design of Micro Phononic Beams Incorporating Size Effects via Tandem Neural Network

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