Adversarially trained LSTMs on reduced order models of urban air pollution simulations

This paper presents an approach to improve computational fluid dynamics simulations forecasts of air pollution using deep learning. Our method, which integrates Principal Components Analysis (PCA) and adversarial training, is a way to improve the forecast skill of reduced order models obtained from the original model solution... (read more)

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METHOD TYPE
Sigmoid Activation
Activation Functions
PCA
Dimensionality Reduction
Tanh Activation
Activation Functions
Memory Network
Working Memory Models
LSTM
Recurrent Neural Networks