Learning the nonlinear dynamics of mechanical metamaterials with graph networks

Xue, Tianju; Adriaenssens, Sigrid; Sheng, Mao

doi:10.1016/j.ijmecsci.2022.107835

Cited by 13 publications

(5 citation statements)

References 44 publications

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“…In addition to modeling parameters and pixels/voxels, other design representations, such as meshes and lattices, can also be used as input to predict effective properties with a specific DL model, like GNNs. [154][155][156]170,171] In GNNs, the prior step is to convert these design representations into a graph representation. Maurizi et al presented a GNN model to predict deformation, stress, and strain fields in various material systems, including fiber and stratified composites and lattice metamaterials (Figure 9a).…”

Section: Predicting Effective Propertiesmentioning

confidence: 99%

Section: Prediction Via Deep Learningmentioning

confidence: 99%

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Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

et al. 2023

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Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time‐consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.This article is protected by copyright. All rights reserved

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Section: Predicting Effective Propertiesmentioning

confidence: 99%

Section: Prediction Via Deep Learningmentioning

confidence: 99%

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

et al. 2023

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“…For example, Guo and Buehler [77] applied GNN to design architected material through a semi-supervised approach. Recently, Xue et al [78] developed a GNN-based framework to predict the nonlinear dynamics of soft mechanical metamaterials.…”

Section: Graph Neural Network (Gnns)mentioning

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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“…Several examples of machine learning , and neural network-powered design tools ,− are also being applied in metamaterial design. Recent publications suggest that new breakthroughs in machine learning have the potential to effectively solve complex design problems, which were previously not possible. , These techniques are usually employed to perform parametric ,, or topology optimization , as well as multiscale optimization, − as a part of surrogate models , or inverse design frameworks. ,− …”

Section: Introductionmentioning

confidence: 99%

“…These methods are also often accompanied by various optimization frameworks employing metaheuristic search algorithms such as gradient-descent, − Bayesian methods, − and nature-inspired algorithms. ,, With regard to the latter category of algorithms, swarm intelligence , and single or multiple objective genetic algorithm-based solutions ,,, are among the most commonly used. Several examples of machine learning , and neural network-powered design tools ,− are also being applied in metamaterial design. Recent publications suggest that new breakthroughs in machine learning have the potential to effectively solve complex design problems, which were previously not possible. , These techniques are usually employed to perform parametric ,, or topology optimization , as well as multiscale optimization, − as a part of surrogate models , or inverse design frameworks. ,− …”

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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Learning the nonlinear dynamics of mechanical metamaterials with graph networks

Cited by 13 publications

References 44 publications

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

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

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