Reduced-space Gaussian Process Regression Forecast for Nonlinear Dynamical Systems

@inproceedings{Wan2016ReducedspaceGP,
  title={Reduced-space Gaussian Process Regression Forecast for Nonlinear Dynamical Systems},
  author={Zhong Yi Wan},
  year={2016}
}
In this thesis work, we formulate a reduced-order data-driven strategy for the efficient probabilistic forecast of complex high-dimensional dynamical systems for which datastreams are available. The first step of this method consists of the reconstruction of the vector field in a reduced-order subspace of interest using Gaussian Process Regression (GPR). GPR simultaneously allows for the reconstruction of the vector field, as well as the estimation of the local uncertainty. The latter is due to… CONTINUE READING