PDgML is a supervised DL model that statistically learns the known or unknown physics of a desired phenomenon by extracting features or attributes from raw training data. PDgML consists of one or a combination of deep neural networks (DNN), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GAN), graph neural networks (GNN), deep reinforcement learning (DRL), Transfomer, deep operator networks, and physics-discovery neural networks.
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