Investigation of Proper Orthogonal Decomposition for Echo State Networks

Proper orthogonal decomposition for parameter estimation in oscillating biological networks

Artificial viscosity proper orthogonal decomposition

We introduce improved reduced-order models for turbulent flows. These models are inspired from successful methodologies used in large eddy simulation, such as artificial viscosity, applied to standardmodels created by proper orthogonal decomposition of flows coupled with Galerkin projection. As a first step in the analysis and testing of our new methodology, we use the Burgers equation with a s...

Proper orthogonal decomposition & kriging strategies for design

Future Work Conclusions The aim of the following research is to therefore remove this obstacle to the application of kriging whilst developing a strategy for use at higher dimensions. The goal of an initial investigation was therefore to determine if these hyperparameters should be tuned after every update, and to what degree they should be tuned, in order for the model to remain effective. Thi...

Proper Orthogonal Decomposition for Optimality Systems

Proper orthogonal decomposition (POD) is a powerful technique for model reduction of non–linear systems. It is based on a Galerkin type discretization with basis elements created from the dynamical system itself. In the context of optimal control this approach may suffer from the fact that the basis elements are computed from a reference trajectory containing features which are quite different ...

Proper orthogonal decomposition of solar photospheric motions.

The spatiotemporal dynamics of the solar photosphere is studied by performing a proper orthogonal decomposition (POD) of line of sight velocity fields computed from high resolution data coming from the MDI/SOHO instrument. Using this technique, we are able to identify and characterize the different dynamical regimes acting in the system. Low-frequency oscillations, with frequencies in the range...