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Found 3 papers, 1 papers with code
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
Our two-step method integrates a Principal Components Analysis (PCA) based adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM) networks.
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
This adversarially trained LSTM-based approach is used on the ROM in order to produce faster forecasts of the air pollution tracer.
Data Assimilation in the Latent Space of a Neural Network
There is an urgent need to build models to tackle Indoor Air Quality issue.
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