Early-Warning Signs for SPDEs with Continuous Spectrum

TL;DR

This paper investigates early-warning signs for SPDEs with continuous spectrum, focusing on variance scaling laws near bifurcation points, providing theoretical rates and numerical validation for different spatial dimensions.

Contribution

It introduces a novel analysis of variance-based early-warning signs for SPDEs with continuous spectrum, deriving precise divergence rates and bounds across dimensions.

Findings

Exact divergence rates for 1D domains.

Upper bounds for 2D and 3D domains.

Numerical simulations confirming theoretical results.

Abstract

In this work, we study early-warning signs for stochastic partial differential equations (SPDEs), where the linearization around a steady state has continuous spectrum. The studied warning sign takes the form of qualitative changes in the variance as a deterministic bifurcation threshold is approached via parameter variation. Specifically, we focus on the scaling law of the variance near the transition. Since we are dealing here, in contrast to previous studies, with the case of continuous spectrum and quantitative scaling laws, it is natural to start with linearizations that are multiplication operators defined by analytic functions. For a one-dimensional spatial domain we obtain precise rates of divergence. In the case of the two- and three-dimensional domains an upper bound to the rate of the early-warning sign is proven. These results are cross-validated by numerical simulations. Our theory can be generically useful for several applications, where stochastic and spatial aspects are important in combination with continuous spectrum bifurcations.