A New Mode Reduction Strategy for the Generalized Kuramoto-Sivashinsky Equation
For researchers studying complex spatiotemporal systems modeled by the gKS equation, this provides a systematic and rigorous mode reduction method that could be applied where classical approaches fail.
The authors develop a rigorous low-dimensional representation of the generalized Kuramoto-Sivashinsky equation using a renormalization group approach with stochastic mode reduction, achieving optimality via maximal information entropy and providing rigorous error bounds. This enables reliable low-dimensional numerical computations by systematically accounting for neglected degrees of freedom.
Consider the generalized Kuramoto-Sivashinsky (gKS) equation. It is a model prototype for a wide variety of physical systems, from flame-front propagation, and more general front propagation in reaction-diffusion systems, to interface motion of viscous film flows. Our aim is to develop a systematic and rigorous low-dimensional representation of the gKS equation. For this purpose, we approximate it by a renormalization group (RG) equation which is qualitatively characterized by rigorous error bounds. This formulation allows for a new stochastic mode reduction guaranteeing optimality in the sense of maximal information entropy. Herewith, noise is systematically added to the reduced gKS equation and gives a rigorous and analytical explanation for its origin. These new results would allow to reliably perform low-dimensional numerical computations by accounting for the neglected degrees of freedom in a systematic way. Moreover, the presented reduction strategy might also be useful in other applications where classical mode reduction approaches fail or are too complicated to be implemented.