scholarly journals Subgrid-scale parametrization of unresolved scales in forced Burgers equation using generative adversarial networks (GAN)

2021 ◽  
Author(s):  
Jeric Alcala ◽  
Ilya Timofeyev
Keyword(s):  
Burgers Equation ◽  
Generative Adversarial Networks ◽  
Subgrid Scale ◽  
Adversarial Networks

scholarly journals Subgrid-scale parametrization of unresolved scales in forced Burgers equation using Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Ilya Timofeyev ◽  
Jeric Alcala
Keyword(s):  
Degrees Of Freedom ◽  
Burgers Equation ◽  
Scale Effects ◽  
Effective Model ◽  
Generative Adversarial Networks ◽  
Subgrid Scale ◽  
Generative Adversarial Network ◽  
Spatial And Temporal Scales ◽  
Ocean Science ◽  
Wide Range

<p>Stochastic subgrid-scale parametrizations aim to incorporate effects of unresolved processes in an effective model by sampling from a distribution usually described in terms of resolved modes. This is an active research area in climate, weather and ocean science where processes evolved in a wide range of spatial and temporal scales. In this study, we evaluate the performance of conditional generative adversarial network (GAN) in parametrizing subgrid-scale effects in a finite-difference discretization of stochastically forced Burgers equation. We define resolved modes as local spatial averages and deviations from these averages are the unresolved degrees of freedom. We train Wesserstein GAN (WGAN) conditioned on the resolved variables to learn the distribution of subgrid flux tendencies for resolved modes and, thus, represent the effect of unresolved scales. Resulting WGAN is then used in an effective model to reproduce the statistical features of resolved modes. We demonstrate that various stationary statistical quantities such as spectrum, moments, autocorrelation, etc. are well approximated by this effective model.</p>

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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