This paper investigates the effectiveness of the recently proposed Gaussian Process Dynamical Model (GPDM) on high dimensional chaotic time series. The GPDM takes a Bayesian approach to modeling high-dimensional time series data, using the Gaussian process Latent Variable model (GPLVM) for nonlinear dimensionality reduction combined with a nonlinear dynamical model in latent space. The GPDM is ...

In the present paper, we discuss the novel concept of super-compressed tensor-structured data formats in high dimensional applications. We describe the multi-folding or quantics based tensor approximation method of O(d logN)-complexity (logarithmic scaling in the volume size), applied to the discrete functions over the product index set {1, ..., N}⊗d, or briefly N -d tensors of size N, and to t...

the main objective in this paper is creating a method for one-dimensional modeling of multi stage axial flow turbine. the calculation used in this technique is based on common thermodynamics and aerodynamics principles in a mean stream line analyses. in this approach, loss models have to be used to determine the entropy increase across each section in the turbine stage. finally, the analysis an...