Koopman Kernels for Learning Dynamical Systems from Trajectory Data

A framework for data-driven modeling and analysis of granular materials in the strongly nonlinear regime using the modern Koopman theory

This research work is about Limited Data Acquisition for the real life physical experiment of fluid flow across cylinder based on Kernelized Extended Dynamic Mode Decomposition by incorporating Gaussian Random Matrix Theory and Laplacian Kernel Function Hilbert space.