no code implementations • 1 Dec 2023 • Ki Sung Jung, Tarek Echekki, Jacqueline H. Chen, Mohammad Khalil
The performance of the reduced-order model with a sparse dataset is found to be remarkably enhanced if the training of the ANN model is restricted by a regularization term that controls the degree of knowledge transfer from source to target tasks.
no code implementations • 6 Apr 2023 • Anuj Kumar, Tarek Echekki
A combustion chemistry acceleration scheme is developed based on deep operator networks (DeepONets).
no code implementations • 5 Apr 2021 • Rishikesh Ranade, Kevin Gitushi, Tarek Echekki
The DeepONet is a machine learning model that is parameterized on the unconditional means of PCs at a given spatial location and discrete PC coordinates and predicts the joint probability density value for the corresponding PC coordinate.
no code implementations • 18 May 2020 • Rishikesh Ranade, Genong Li, Shaoping Li, Tarek Echekki
In this work, we address these issues by introducing an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks.