transfer learning
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.
A transfer learning metamodel using deep neural networks applied to natural convection flows in enclosures
We develop a framework for transfer learning with deep neural networks for natural convection. We consider the benchmark problem of an air-filled enclosure and train our network to predict the heat transfer rate for different Rayleigh inputs. We then transfer the learning to consider enclosures filled with an arbitrary fluid.
A multi-grid simulation framework for metamodeling by artificial neural networks
The numerical simulations using coarse grid systems may provide precise results for a limited range of input parameters. As such, we utilize a multi-grid dataset for training an ANN in order to reduce simulation times. We denoised the dataset, and retrained the ANN based on abnormalities observed in training losses.