Reaction-diffusion approximation of nonlocal interactions in high-dimensional Euclidean space (2504.15180v1)

Abstract: In various phenomena such as pattern formation, neural firing in the brain and cell migration, interactions that can affect distant objects globally in space can be observed. These interactions are referred to as nonlocal interactions and are often modeled using spatial convolution with an appropriate integral kernel. Many evolution equations incorporating nonlocal interactions have been proposed. In such equations, the behavior of the system and the patterns it generates can be controlled by modifying the shape of the integral kernel. However, the presence of nonlocality poses challenges for mathematical analysis. To address these difficulties, we develop an approximation method that converts nonlocal effects into spatially local dynamics using reaction-diffusion systems. In this paper, we present an approximation method for nonlocal interactions in evolution equations based on a linear sum of solutions to a reaction-diffusion system in high-dimensional Euclidean space up to three dimensions. The key aspect of this approach is identifying a class of integral kernels that can be approximated by a linear combination of specific Green functions in the case of high-dimensional spaces. This enables us to demonstrate that any nonlocal interactions can be approximated by a linear sum of auxiliary diffusive substances. Our results establish a connection between a broad class of nonlocal interactions and diffusive chemical reactions in dynamical systems.