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Stimuli‐Responsive Materials: Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials (Adv. Theory Simul. 7/2020)
Author(s) -
Zhang Zhizhou,
Gu Grace X.
Publication year - 2020
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.202070017
Subject(s) - convolutional neural network , deformation (meteorology) , finite element method , computer science , deep learning , artificial intelligence , materials science , structural engineering , engineering , composite material
Digital materials are programmable smart composites that exhibit complex deformation when experiencing external stimuli. In article number 2000031, Zhizhou Zhang and Grace X. Gu report work that integrates multi‐physics modeling and machine learning (convolutional neural networks) to accelerate the prediction of the non‐linear deformation behavior of stimuli‐responsive digital materials. Moreover, high‐level design principles can be extracted from the model using sensitivity analysis.