Deep learning for topology optimization of 2D metamaterials
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TLDR
A deep learning model based on a convolutional neural network that predicts optimal meetamaterial designs and non-iteratively optimizes metamaterials for either maximizing the bulk modulus, maximizing the shear modulus or minimizing the Poisson's ratio.About:
This article is published in Materials & Design.The article was published on 2020-11-01 and is currently open access. It has received 158 citations till now. The article focuses on the topics: Topology optimization & Metamaterial.read more
Citations
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Deep learning for plasticity and thermo-viscoplasticity
TL;DR: This paper applied a variety of sequence learning models to almost instantly predict the history-dependent responses (stresses and energy) of a class of cellular materials as well as the multiphysics problem of steel solidification with multiple thermo-viscoplasticity constitutive models accounting for substantial temperature, time and path dependencies, and phase transformation.
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A review of artificial neural networks in the constitutive modeling of composite materials
TL;DR: A state-of-the-art literature review of ANN models in the constitutive modeling of composite materials, focusing on discovering unknown constitutive laws and accelerating multiscale modeling is given.
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Additive manufacturing of polymeric composites from material processing to structural design
TL;DR: In this paper, the authors provide a comprehensive guide to the stakeholders who want to utilize or develop an additive manufacturing process for polymeric composites and provide an outlook on future research opportunities on AM-fabricated composites from design to fabrication.
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From Photonic Crystals to Seismic Metamaterials: A Review via Phononic Crystals and Acoustic Metamaterials
TL;DR: In this paper, the authors discuss the historical context, current progresses and possible future outcomes of metamaterials and highlight the interesting phenomena observed in optics/electromagnetic metammaterials with acoustic and elastic counterparts.
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Deep learning framework for material design space exploration using active transfer learning and data augmentation
TL;DR: In this paper, a deep neural network-based forward design approach is proposed to enable an efficient search for superior materials far beyond the domain of the initial training set, which compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Book
Cellular Solids: Structure and Properties
Lorna J. Gibson,Michael F. Ashby +1 more
TL;DR: The linear elasticity of anisotropic cellular solids is studied in this article. But the authors focus on the design of sandwich panels with foam cores and do not consider the properties of the materials.
Journal ArticleDOI
Generating optimal topologies in structural design using a homogenization method
Martin P. Bendsøe,Noboru Kikuchi +1 more
TL;DR: In this article, the authors present a methodology for optimal shape design based on homogenization, which is related to modern production techniques and consists of computing the optimal distribution in space of an anisotropic material that is constructed by introducing an infimum of periodically distributed small holes in a given homogeneous, i.i.
Journal ArticleDOI
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
TL;DR: In this article, the authors introduce physics-informed neural networks, which are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations.