Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine
Increasing the application of machine learning algorithms in engine development has the potential to reduce the number of experimental runs and the computation cost of computational fluid dynamics simulations. The objective of this study is to assess if such a statistical modelling approach can predict engine efficiency and emissions at any given condition for an already calibrated spark ignition
