物理
耗散系统
活性物质
自组织
统计物理学
二次方程
激光器
计算机科学
人工智能
光学
数学
量子力学
生物
细胞生物学
几何学
作者
Eduardo Brandao,Anthony Nakhoul,Stefan Duffner,Rémi Emonet,Florence Garrelie,Amaury Habrard,François Jacquenet,Florent Pigeon,Marc Sebban,Jean‐Philippe Colombier
标识
DOI:10.1103/physrevlett.130.226201
摘要
Ultrafast laser irradiation can induce spontaneous self-organization of surfaces into dissipative structures with nanoscale reliefs. These surface patterns emerge from symmetry-breaking dynamical processes that occur in Rayleigh-Bénard-like instabilities. In this study, we demonstrate that the coexistence and competition between surface patterns of different symmetries in two dimensions can be numerically unraveled using the stochastic generalized Swift-Hohenberg model. We originally propose a deep convolutional network to identify and learn the dominant modes that stabilize for a given bifurcation and quadratic model coefficients. The model is scale-invariant and has been calibrated on microscopy measurements using a physics-guided machine learning strategy. Our approach enables the identification of experimental irradiation conditions for a desired self-organization pattern. It can be generally applied to predict structure formation in situations where the underlying physics can be approximately described by a self-organization process and data is sparse and nontime series. Our Letter paves the way for supervised local manipulation of matter using timely controlled optical fields in laser manufacturing.
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