Transfer learning for predicting source terms of principal component
  transport in chemically reactive flow
Ki Sung Jung Tarek Echekki Jacqueline H. Chen Mohammad Khalil
The objective of this study is to evaluate whether the number of requisite
training samples can be reduced with the use of various transfer learning
models for predicting, for example, the chemical source terms of the
data-driven reduced-order model that represents the homogeneous ignition
process of a hydrogen/air mixture. Principal component analysis is applied to
reduce the dimensionality of the hydrogen/air mixture in composition space.
Artificial neural networks (ANNs) are used to tabulate the reaction rates of
principal components, and subsequently, a system of ordinary differential
equations is solved. As the number of training samples decreases at the target
task (i.e.,for T0 > 1000 K and various phi), the reduced-order model fails to
predict the ignition evolution of a hydrogen/air mixture. Three transfer
learning strategies are then applied to the training of the ANN model with a
sparse dataset. 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. To this end, a novel transfer learning
method is introduced, parameter control via partial initialization and
regularization (PaPIR), whereby the amount of knowledge transferred is
systemically adjusted for the initialization and regularization of the ANN
model in the target task. It is found that an additional performance gain can
be achieved by changing the initialization scheme of the ANN model in the
target task when the task similarity between source and target tasks is
relatively low.