Temperature-conditioned deep generative framework for scalable Ising spin configuration synthesis with physics-informed constraints.

This study presents a temperature-conditioned deep generative approach for efficiently generating spin configurations of the two-dimensional (2D) Ising model. The method employs a convolutional generator network, conditioned on spin states, temperatu…

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