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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Latent GAN: Using a Latent Space-Based GAN for Rapid Forecasting of CFD Models
verfasst von : Jamal Afzali, César Quilodrán Casas, Rossella Arcucci
Erschienen in: Computational Science – ICCS 2021
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Abstract
The focus of this study is to simulate realistic fluid flow, through Machine Learning techniques that could be utilised in real-time forecasting of urban air pollution. We propose a novel Latent GAN architecture which looks at combining an AutoEncoder with a Generative Adversarial Network to predict fluid flow at the proceeding timestep of a given input, whilst keeping computational costs low. This architecture is applied to tracer flows and velocity fields around an urban city. We present a pair of AutoEncoders capable of dimensionality reduction of 3 orders of magnitude. Further, we present a pair of Generator models capable of performing real-time forecasting of tracer flows and velocity fields. We demonstrate that the models, as well as the latent spaces generated, learn and retain meaningful physical features of the domain. Despite the domain of this project being that of computational fluid dynamics, the Latent GAN architecture is designed to be generalisable such that it can be applied to other dynamical systems.