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Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation.
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Pytorch: An imperative style, high-performance deep learning library
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
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Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of black–scholes partial differential equations
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Physics-informed generative adversarial networks for stochastic differential equations
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Neural sdes as infinite-dimensional gans
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