scholarly journals Designing complex architectured materials with generative adversarial networks

2020 ◽  
Vol 6 (17) ◽  
pp. eaaz4169 ◽  
Author(s):  
Yunwei Mao ◽  
Qi He ◽  
Xuanhe Zhao
Keyword(s):  
Prior Knowledge ◽  
Mass Production ◽  
Systematic Approach ◽  
Elastic Stiffness ◽  
Upper Bounds ◽  
Generative Adversarial Networks ◽  
Simulation Data ◽  
Adversarial Networks ◽  
Isotropic Elasticity ◽  
Architectured Materials

Architectured materials on length scales from nanometers to meters are desirable for diverse applications. Recent advances in additive manufacturing have made mass production of complex architectured materials technologically and economically feasible. Existing architecture design approaches such as bioinspiration, Edisonian, and optimization, however, generally rely on experienced designers’ prior knowledge, limiting broad applications of architectured materials. Particularly challenging is designing architectured materials with extreme properties, such as the Hashin-Shtrikman upper bounds on isotropic elasticity in an experience-free manner without prior knowledge. Here, we present an experience-free and systematic approach for the design of complex architectured materials with generative adversarial networks. The networks are trained using simulation data from millions of randomly generated architectures categorized based on different crystallographic symmetries. We demonstrate modeling and experimental results of more than 400 two-dimensional architectures that approach the Hashin-Shtrikman upper bounds on isotropic elastic stiffness with porosities from 0.05 to 0.75.

scholarly journals Blind Image Separation Method Based on Cascade Generative Adversarial Networks

2021 ◽  
Vol 11 (20) ◽  
pp. 9416
Author(s):  
Fei Jia ◽  
Jindong Xu ◽  
Xiao Sun ◽  
Yongli Ma ◽  
Mengying Ni
Keyword(s):  
Prior Knowledge ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Adversarial Networks ◽  
Training Samples ◽  
Image Separation ◽  
Blind Image ◽  
Insufficient Number ◽  
Image Datasets

To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.

scholarly journals Knowledge-Based Regularization in Generative Modeling

2020 ◽  
Author(s):  
Naoya Takeishi ◽  
Yoshinobu Kawahara
Keyword(s):  
Prior Knowledge ◽  
Domain Knowledge ◽  
Specific Model ◽  
General Purpose ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Knowledge Based ◽  
Adversarial Networks ◽  
Generative Modeling ◽  
Relative Dependence

Prior domain knowledge can greatly help to learn generative models. However, it is often too costly to hard-code prior knowledge as a specific model architecture, so we often have to use general-purpose models. In this paper, we propose a method to incorporate prior knowledge of feature relations into the learning of general-purpose generative models. To this end, we formulate a regularizer that makes the marginals of a generative model to follow prescribed relative dependence of features. It can be incorporated into off-the-shelf learning methods of many generative models, including variational autoencoders and generative adversarial networks, as its gradients can be computed using standard backpropagation techniques. We show the effectiveness of the proposed method with experiments on multiple types of datasets and generative models.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

A Primer on Generative Adversarial Networks

2020 ◽  
Author(s):  
Dr. Vikas Thada ◽  
Mr. Utpal Shrivastava ◽  
Jyotsna Sharma ◽  
Kuwar Prateek Singh ◽  
Manda Ranadeep
Keyword(s):  
Generative Adversarial Networks ◽  
Adversarial Networks
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