Urban air pollution forecasts generated from latent space representationDownload PDF

Feb 26, 2020 (edited Apr 26, 2020)ICLR 2020 Workshop DeepDiffEq Blind SubmissionReaders: Everyone
  • TL;DR: Fast forecast of unstructured mesh CFDs with a two-step dimension reduction using PCA and autoencoders
  • Abstract: This paper presents an approach to replicate computational fluid dynamics simulations of air pollution using deep learning. The study area is in London, where a tracer aims to replicate a busy traffic junction. Our method, which integrates Principal Components Analysis (PCA) and autoencoders (AE), is a computationally cheaper way to generate a latent space representation of the original unstructured mesh model. Once the PCA is applied on the original model solution, a Fully-Connected AE is trained on the full-rank PCs. This yields a compression of the original data by $10^{6}$. The number of trainable parameters is also reduced using this method. A LSTM-based approach is used on the latent space to produce faster forecasts of the air pollution tracer.
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