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• W4387562857 abstract "Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques." @default.
• W4387562857 created "2023-10-13" @default.
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• W4387562857 date "2023-10-12" @default.
• W4387562857 modified "2023-10-18" @default.
• W4387562857 title "Accurate generation of stochastic dynamics based on multi-model generative adversarial networks" @default.
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• W4387562857 doi "https://doi.org/10.1063/5.0170307" @default.
• W4387562857 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37823464" @default.
• W4387562857 hasPublicationYear "2023" @default.
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