Abstract
Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. We investigate if GANs can also be used to approximate one-dimensional It stochastic differential equations (SDEs). We propose a scheme that approximates the path-wise conditional distribution of SDEs for large time
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