Bayesian modeling of pattern formation from one snapshot of pattern

Natsuhiko Yoshinaga, Satoru Tokuda

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)


Partial differential equations (PDEs) 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 method, referred to as Bayesian modeling of PDEs (BM-PDEs), to estimate the best dynamical PDE for one snapshot of a objective pattern under the stationary state without ground truth. We apply BM-PDEs to nontrivial patterns, such as quasicrystals (QCs), a double gyroid, and Frank-Kasper structures. We also generate three-dimensional dodecagonal QCs from a PDE model. This is done by using the estimated parameters for the Frank-Kasper A15 structure, which closely approximates the local structures of QCs. Our method works for noisy patterns and the pattern synthesized without the ground-truth parameters, which are required for the application toward experimental data.

ジャーナルPhysical Review E
出版ステータス出版済み - 12月 2022

!!!All Science Journal Classification (ASJC) codes

  • 統計物理学および非線形物理学
  • 統計学および確率
  • 凝縮系物理学


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