  We propose a machine learning approach aiming at reducing Bond Graphs. The
output of the machine learning is a hybrid modeling that contains a reduced
Bond Graph coupled to a simple artificial neural network. The proposed coupling
enables knowledge continuity in machine learning. In this paper, a neural
network is obtained by a linear calibration procedure. We propose a method that
contains two training steps. First, the method selects the components of the
original Bond Graph that are kept in the Reduced Bond Graph. Secondly, the
method builds an artificial neural network that supplements the reduced Bond
Graph. Because the output of the machine learning is a hybrid model, not solely
data, it becomes difficult to use a usual Backpropagation Through Time to
calibrate the weights of the neural network. So, in a first attempt, a very
simple neural network is proposed by following a model reduction approach. We
consider the modeling of the automotive cabins thermal behavior. The data used
for the training step are obtained via solutions of differential algebraic
equations by using a design of experiment. Simple cooling simulations are run
during the training step. We show a simulation speed-up when the reduced bond
graph is used to simulate the driving cycle of the WLTP vehicles homologation
procedure, while preserving accuracy on output variables. The variables of the
original Bond Graph are split into a set of primary variables, a set of
secondary variables and a set of tertiary variables. The reduced bond graph
contains all the primary variables, but none of the tertiary variables.
Secondary variables are coupled to primary ones via an artificial neural
network. We discuss the extension of this coupling approach to more complex
artificial neural networks.
