Brief laser pulses can induce autonomous organization of nanostructures pattern without external guidance. This interaction between a laser light and a material is governed by Maxwell's equations. These equations provide the theoretical framework for understanding how electromagnetic waves propagate and interact with matter. The Finite-Difference Time-Domain (FDTD) method models the laser-material interactions, providing insights into absorption, reflection, and scattering over time, ultimately contributing to self-organization within the material. Despite a theoretical understanding, there is no reliable model to predict the self-organization process responsible for the nanostructures. Our work addresses this issue by aiming to predict the surface changes after multiple laser irradiations using neural networks. Deep learning models have undergone advancements and prove suitable for extracting meaningful insights and simulating physical processes. This combination of laser physics and deep learning offer a promising approach to improve our ability to control nanostructures formation on materials.
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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