Month: November 2020
ANN optimization for building metamodels exploiting a variable batch size training and hyperparameter tuning
We describe an approach to find optimized ANN architectures for building metamodels using the Keras library with TensorFlow as the backend. We use the Hyperband algorithm to optimize the hyperparameters of our model on top of a brute-force tuning by a variable-batch training.
A deep learning metamodel for predicting natural convection in hollow enclosures
By transferring the learning from a metamodel that was already trained to predict the heat transfer rate for natural convection flows in enclosures, we construct a deep neural network to account for enclosures with a centered hole. We demonstrate that a transfer learning approach reduces the computational and training costs.