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We present a deep learning algorithm for the numerical solution of parametric families of high-dimensional linear Kolmogorov partial differential equations (PDEs).
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DGM: A deep learning algorithm for solving partial differential equations
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Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ
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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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