Model of rough surfaces with Gaussian processes

Abstract Surface roughness plays a critical role and has effects in, e.g. fluid dynamics or contact mechanics. For example, to evaluate behavior at different properties, real-world numerical experiments are performed. Numerical simulations of rough surfaces can speed up these studies because they help collect more relevant information. However, it is hard simulate with deterministic structured components in current methods. In this work, we present novel approach Gaussian process (GP) noise model GPs periodic elements. generalize traditional methods not restricted stationarity so wider range surfaces. paper, summarize the theoretical similarities auto-regressive moving-average processes introduce linear view GPs. We also show examples ground honed simulated by predefined model. The proposed method be used fit measurement data surface. particular, demonstrate turned profiles that inherently periodic.