Understanding spontaneous pattern emergence on laser-irradiated materials is a long-standing interest. Periodic surface structures arise from multiphysical coupling: electromagnetics, nonlinear optics, plasmonics, fluid dynamics, or thermochemical reactions. Multi-shot irradiation with ultrafast laser pulses generates stable periodic patterns arising from localized perturbations influenced by disturbances and nonlinear saturation. Describing pattern growth requires nonlinear dynamics beyond classic equations. The challenge is developing an efficient model with symmetry breaking, scale invariance, stochasticity, and nonlinear properties to reproduce dissipative structures. Stochastic Swift-Hohenberg modeling replicates hydrodynamic fluctuations near the convective instability threshold, inherent in laser-induced self-organized nanopatterns. We will demonstrate that a deep convolutional networks can learn pattern complexity, connecting model coefficients to experimental parameters for designing specific patterns. The model predicts patterns accurately, even with limited non-time series data. It identifies laser parameter regions and could predict novel patterns independently.
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