Bayesian Modelling of Pattern Formation from One Snapshot of Pattern

Abstract

Partial differential equations (PDE) have been widely used to reproduce patterns in nature and to give insight into the mechanism underlying pattern formation. Although many PDE models have been proposed, they rely on the pre-request knowledge of physical laws and symmetries, and developing a model to reproduce a given desired pattern remains difficult. We propose a novel method, referred to as Bayesian modelling of PDE (BM-PDE), to estimate the best dynamical PDE for one snapshot of a target pattern under the stationary state without ground truth. We apply BM-PDE to nontrivial patterns,such as quasi-crystals (QCs), a double gyroid and Frank Kasper structures. By using the estimated parameters for the approximant of QCs, we successfully generate, for the first time,three-dimensional dodecagonal QCs from a PDE model. Our method works for noisy patterns and the pattern synthesised without the ground truth parameters, which are required for the application toward experimental data.