Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model

Variational autoencoders (VAE) are machine learning models that can extract low dimensional representations of data from datasets of high complexity and volume. Importantly, they can be used for generative purposes to reconstruct complex data, such as images, from a low dimensional encoding of only...

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Autores principales:	Andrew J. Lew, Markus J. Buehler
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Mechanics Design Machine learning 3D printing Autoencoder Topology optimization Mechanics of engineering. Applied mechanics TA349-359 Technology T
Acceso en línea:	https://doaj.org/article/7f81720314774945a003abb4058ec274
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id	oai:doaj.org-article:7f81720314774945a003abb4058ec274
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spelling	oai:doaj.org-article:7f81720314774945a003abb4058ec2742021-12-04T04:36:02ZEncoding and exploring latent design space of optimal material structures via a VAE-LSTM model2666-359710.1016/j.finmec.2021.100054https://doaj.org/article/7f81720314774945a003abb4058ec2742021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666359721000457https://doaj.org/toc/2666-3597Variational autoencoders (VAE) are machine learning models that can extract low dimensional representations of data from datasets of high complexity and volume. Importantly, they can be used for generative purposes to reconstruct complex data, such as images, from a low dimensional encoding of only a few variables. Long short-term memory (LSTM) neural networks are well suited to learning logical trajectory relationships within datasets. Using these two models in concert, we develop a VAE-LSTM approach to learn a classic mechanical materials design problem. Here, we focus on the compliance optimization of cantilever design, using a VAE to encode cantilever structures into a 2D latent space and a LSTM to learn trajectories in that latent space corresponding to the optimization process. Ultimately, we are able to clearly visualize the space of cantilever design, generate new design with extremely low density beyond the original dataset, and obtain optimal cantilever structures inspired by nature. We also demonstrate how the resulting designs can be manufactured using 3D printing, offering a rapid pathway from concept to prototype. The method we developed here can be generalized to other image-based datasets encapsulating changes from multiple factors. The ability offered by our approach to interpret complex behavior, via representations in simplified space, has great potential for application in the intelligent design and manufacturing of materials structure problems.Andrew J. LewMarkus J. BuehlerElsevierarticleMechanicsDesignMachine learning3D printingAutoencoderTopology optimizationMechanics of engineering. Applied mechanicsTA349-359TechnologyTENForces in Mechanics, Vol 5, Iss , Pp 100054- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mechanics Design Machine learning 3D printing Autoencoder Topology optimization Mechanics of engineering. Applied mechanics TA349-359 Technology T
spellingShingle	Mechanics Design Machine learning 3D printing Autoencoder Topology optimization Mechanics of engineering. Applied mechanics TA349-359 Technology T Andrew J. Lew Markus J. Buehler Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model
description	Variational autoencoders (VAE) are machine learning models that can extract low dimensional representations of data from datasets of high complexity and volume. Importantly, they can be used for generative purposes to reconstruct complex data, such as images, from a low dimensional encoding of only a few variables. Long short-term memory (LSTM) neural networks are well suited to learning logical trajectory relationships within datasets. Using these two models in concert, we develop a VAE-LSTM approach to learn a classic mechanical materials design problem. Here, we focus on the compliance optimization of cantilever design, using a VAE to encode cantilever structures into a 2D latent space and a LSTM to learn trajectories in that latent space corresponding to the optimization process. Ultimately, we are able to clearly visualize the space of cantilever design, generate new design with extremely low density beyond the original dataset, and obtain optimal cantilever structures inspired by nature. We also demonstrate how the resulting designs can be manufactured using 3D printing, offering a rapid pathway from concept to prototype. The method we developed here can be generalized to other image-based datasets encapsulating changes from multiple factors. The ability offered by our approach to interpret complex behavior, via representations in simplified space, has great potential for application in the intelligent design and manufacturing of materials structure problems.
format	article
author	Andrew J. Lew Markus J. Buehler
author_facet	Andrew J. Lew Markus J. Buehler
author_sort	Andrew J. Lew
title	Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model
title_short	Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model
title_full	Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model
title_fullStr	Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model
title_full_unstemmed	Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model
title_sort	encoding and exploring latent design space of optimal material structures via a vae-lstm model
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7f81720314774945a003abb4058ec274
work_keys_str_mv	AT andrewjlew encodingandexploringlatentdesignspaceofoptimalmaterialstructuresviaavaelstmmodel AT markusjbuehler encodingandexploringlatentdesignspaceofoptimalmaterialstructuresviaavaelstmmodel
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Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model

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