The advancement of Machine-learning (ML) methods such as Gaussian Process Regressions (GPR) have enabled the development and use of Reduced Order Models for complex automotive dynamic systems, as alternatives to conventional parametric methods or multi-dimensional look-up tables. GPR provides a mathematical framework for probabilistic representation of complex non-linear system. This paper discusses the use of GPR to characterize nonlinear dynamic behavior of an engine disconnect clutch used in a P2 hybrid propulsion architecture for efficient in-vehicle deployment, under computational and memory resources constraints.