Digital twins of human corneal endothelium from generative adversarial networks

2021 ◽  
Author(s):  
Eloi Dussy Lachaud ◽  
Andrew Caunes ◽  
Gilles Thuret ◽  
Yann Gavet
Keyword(s):  
Corneal Endothelium ◽  
Generative Adversarial Networks ◽  
Human Corneal Endothelium ◽  
Adversarial Networks ◽  
Digital Twins

scholarly journals On generative models as the basis for digital twins

2021 ◽  
Vol 2 ◽  
Author(s):  
George Tsialiamanis ◽  
David J. Wagg ◽  
Nikolaos Dervilis ◽  
Keith Worden
Keyword(s):  
Linear Structure ◽  
Generative Models ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Deterministic Models ◽  
Adversarial Networks ◽  
Digital Twins ◽  
Different Types ◽  
Potential Use ◽  
Model Approach

Abstract A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modeling applications. Two different types of generative models are considered here. The first is a physics-based model based on the stochastic finite element (SFE) method, which is widely used when modeling structures that have material and loading uncertainties imposed. Such models can be calibrated according to data from the structure and would be expected to outperform any other model if the modeling accurately captures the true underlying physics of the structure. The potential use of SFE models as digital mirrors is illustrated via application to a linear structure with stochastic material properties. For situations where the physical formulation of such models does not suffice, a data-driven framework is proposed, using machine learning and conditional generative adversarial networks (cGANs). The latter algorithm is used to learn the distribution of the quantity of interest in a structure with material nonlinearities and uncertainties. For the examples considered in this work, the data-driven cGANs model outperforms the physics-based approach. Finally, an example is shown where the two methods are coupled such that a hybrid model approach is demonstrated.

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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