Leveraging Hamiltonian Structure for Accurate Uncertainty Propagation

TL;DR

This paper introduces a novel method leveraging Hamiltonian structure to improve the accuracy of uncertainty propagation in nonlinear dynamical systems, effectively managing basis function growth and utilizing sparse approximation.

Contribution

The paper presents a Hamiltonian-based approach for uncertainty propagation that reduces basis function complexity and employs sparse methods for basis selection.

Findings

Effective uncertainty propagation in nonlinear systems

Reduced basis function growth with system dimension

Successful application to oscillator and two-body problems

Abstract

In this work, we leverage the Hamiltonian kind structure for accurate uncertainty propagation through a nonlinear dynamical system. The developed approach utilizes the fact that the stationary probability density function is purely a function of the Hamiltonian of the system. This fact is exploited to define the basis functions for approximating the solution of the Fokker-Planck-Kolmogorov equation. This approach helps in curtailing the growth of basis functions with the state dimension. Furthermore, sparse approximation tools have been utilized to automatically select appropriate basis functions from an over-complete dictionary. A nonlinear oscillator and two-body problem are considered to show the efficacy of the proposed approach. Simulation results show that such an approach is effective in accurately propagating uncertainty through non-conservative as well as conservative systems.