Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm

Wang, Yongzhen; Zeng, Qiang; Wang, Jizhen; Liu, Ying; Fang, Daining

doi:10.1016/j.cma.2022.115571

Cited by 70 publications

(18 citation statements)

References 58 publications

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“…Along this line, ML is becoming an important tool to systematically design these novel architected metamaterials with desired properties and functionality beyond laboratory trial-and-error [43,257,258,259,260,261]. Comprehensive review papers have been published in recent years that reviewed and discussed the methodology and applications of ML in architected material design [31,262,263,264]. Herein, in this subsection, we will focus on reviewing recent advances in experimental efforts in ML-enabled design of architected materials.…”

Section: For Architected Materialsmentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano-and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

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Section: For Architected Materialsmentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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“…These methods are also often accompanied by various optimization frameworks employing metaheuristic search algorithms such as gradient-descent, 32−34 Bayesian methods, 35−37 and natureinspired algorithms. 13,38,39 With regard to the latter category of algorithms, swarm intelligence 40,41 and single or multiple objective genetic algorithm-based solutions 28,38,42,43 are among the most commonly used. Several examples of machine learning 28,44 and neural network-powered design tools 11,45−48 are also being applied in metamaterial design.…”

Section: Introductionmentioning

confidence: 99%

Cubically Symmetric Mechanical Metamaterials Projected from 4th-Dimensional Geometries Reveal High Specific Properties in Shear

Cerniauskas,

Alam

2023

ACS Appl. Eng. Mater.

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In this paper, we present an emerging class of cubically symmetric mechanical metamaterials based on 3-space geometrical shadows of 4th-dimensional geometries (4-polytopes) that are optimized for high shear resistance and minimized weight. We show that by employing a genetic algorithm-based optimization framework, the mechanical metamaterials can achieve an increase of more than 40-fold in their specific shear properties. Experimental results reveal that the metamaterial structure with the highest specific shear resistance, the 5-cell (pentatope), exhibits specific shear stiffness that is almost 2-fold higher than that of a gyroid, while the 8-cell (tesseract) structure exhibits the highest specific shear yield strength that is 2.4 times higher than that of a hexagonal honeycomb tested in the out-of-plane direction.

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“…[ 12 ] However, these methods are less suitable for inverse design tasks as they typically require high number of evolutions and have limited design exploration capabilities. [ 13–16 ] Furthermore, these algorithms necessitate the constant execution of physics‐based simulations to evaluate the objective function, adding to their computational demands and making them less efficient for designing complex metamaterial systems. [ 17,18 ]…”

Section: Introductionmentioning

confidence: 99%

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Sarkar,

Ji,

Jermain

et al. 2023

Advanced Photonics Research

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Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.

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Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm

Cited by 70 publications

References 58 publications

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Cubically Symmetric Mechanical Metamaterials Projected from 4th-Dimensional Geometries Reveal High Specific Properties in Shear

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

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