A probabilistic decomposition-synthesis method for the quantification of rare events due to internal instabilities

TL;DR

This paper introduces a probabilistic decomposition-synthesis method to efficiently quantify rare, heavy-tailed events in dynamical systems caused by internal instabilities, combining Gaussian and non-Gaussian analysis for accurate tail estimation.

Contribution

The paper presents a novel computational approach that decomposes system statistics into Gaussian and heavy-tailed components, enabling efficient quantification of rare events in complex nonlinear systems.

Findings

Accurately quantifies non-Gaussian tails beyond 10 standard deviations.

Applies the method to coupled oscillators and water wave models.

Achieves results at a fraction of Monte Carlo simulation costs.

Abstract

We consider the problem of probabilistic quantification of dynamical systems that have heavy-tailed characteristics. These heavy-tailed features are associated with rare transient responses due to the occurrence of internal instabilities. Here we develop a computational method, a probabilistic decomposition-synthesis technique, that takes into account the nature of internal instabilities to inexpensively determine the non-Gaussian probability density function for any arbitrary quantity of interest. Our approach relies on the decomposition of the statistics into a `non-extreme core', typically Gaussian, and a heavy-tailed component. This decomposition is in full correspondence with a partition of the phase space into a `stable' region where we have no internal instabilities, and a region where non-linear instabilities lead to rare transitions with high probability. We quantify the statistics in the stable region using a Gaussian approximation approach, while the non-Gaussian distributions associated with the intermittently unstable regions of the phase space are inexpensively computed through order-reduction methods that take into account the strongly nonlinear character of the dynamics. The probabilistic information in the two domains is analytically synthesized through a total probability argument. The proposed approach allows for the accurate quantification of non-Gaussian tails at more than 10 standard deviations, at a fraction of the cost associated with the direct Monte-Carlo simulations. We demonstrate the probabilistic decomposition-synthesis method for rare events for two dynamical systems exhibiting extreme events: a two-degree-of-freedom system of nonlinearly coupled oscillators, and in a nonlinear envelope equation characterizing the propagation of unidirectional water waves.