IH-GAN: A conditional generative model for implicit surface-based inverse design of cellular structures

Wang, Jun; Chen, Wei; Fuge, Mark; Rai, Rahul

doi:10.1016/j.cma.2022.115060

Cited by 33 publications

(26 citation statements)

References 75 publications

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“…It is often measured through geometric/mechanical similarities at the interface of neighboring unit cells. [62,63]…”

Section: Key Conceptsmentioning

confidence: 99%

“…Sometimes such a surrogate is incorporated into a larger network architecture and trained end-to-end together with other components. [63,66]…”

Section: Supervised Learningmentioning

confidence: 99%

“…[73] While VAEs and GANs have been generally used for unsupervised learning, prior works have proposed variants of these models, such as conditional generative adversarial networks (cGAN) (e.g., refs. [63,64,74], conditional variational autoencoders (cVAE) (e.g., refs. [75,76]), and VAE-regressor models (e.g., ref.…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…[66]), that require supervised learning. These supervised learning models either enable the inverse design of metamaterials [63,64,[74][75][76] or help construct a property-related metamaterial latent representation. [66]…”

Section: Unsupervised Learningmentioning

confidence: 99%

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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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“…It is often measured through geometric/mechanical similarities at the interface of neighboring unit cells. [62,63]…”

Section: Key Conceptsmentioning

confidence: 99%

“…Sometimes such a surrogate is incorporated into a larger network architecture and trained end-to-end together with other components. [63,66]…”

Section: Supervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

See 2 more Smart Citations

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

Lee,

Chen,

Wang

et al. 2023

Advanced Materials

Self Cite

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“…The recent development of generative models in machine learning (ML) provides ideas on how to tackle this issue, with variational autoencoders (VAEs) [18] and generative adversarial networks (GANs) [10] having shown promising results for the generation of mechanical metamaterials [34,35]. These models learn a transformation to a latent space, where each point corresponds to a valid design.…”

Section: Introductionmentioning

confidence: 99%

Controlling auxeticity in curved-beam metamaterials via a deep generative model

Felsch¹,

Ghavidelnia²,

Schwarz³

et al. 2023

Preprint

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Lattice-based mechanical metamaterials are known to exhibit quite a unique mechanical behavior owing to their rational internal architecture. This includes unusual properties such as a negative Poisson's ratio, which can be easily tuned in reentrant-hexagonal metamaterials by adjusting the angles between beams. However, changing the angles also affects the overall dimensions of the unit cell. We show that by replacing traditional straight beams with curved ones, it is possible to control the Poisson's ratio of reentranthexagonal metamaterials without affecting their overall dimensions. While the mechanical properties of these structures can be predicted through finite element simulations or, in some cases, analytically, many applications require to identify architectures with specific target properties. To solve this inverse problem, we introduce a deep learning framework for generating metamaterials with desired properties. By supplying the generative model with a guide structure in addition to the target properties, we are not only able to generate a large number of alternative architectures with the same properties, but also to express preference for a specific shape. Deep learning predictions together with experimental measurements prove that this approach allows us to accurately generate unit cells fitting specific properties for curved-beam metamaterials.

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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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IH-GAN: A conditional generative model for implicit surface-based inverse design of cellular structures

Cited by 33 publications

References 75 publications

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

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

Controlling auxeticity in curved-beam metamaterials via a deep generative model

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

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