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Relying on the classical connection between Backward Stochastic Differential Equations (BSDEs) and non-linear parabolic partial differential equations (PDEs), we propose a new probabilistic learning scheme for solving high-dimensional semi-linear parabolic PDEs.
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On some non asymptotic bounds for the euler scheme
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On the convergence rate of sparse grid least squares regression
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Improved error bounds for quantization based numerical schemes for bsde and nonlinear filtering
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Branching diffusion representation of semilinear pdes and monte carlo approximation
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An overview on deep learning-based approximation methods for partial differential equations
Beck, C., Hutzenthaler, M., Jentzen, A., and Kuckuck, B · 2020
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Selectnet: Self-paced learning for high-dimensional partial differential equations
Gu, Y., Yang, H., and Zhou, C · 2020
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Algorithms for solving high dimensional pdes: From nonlinear monte carlo to machine learning
Han, J., Jentzen, A., and E, W · 2020
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Convergence of the deep bsde method for coupled fbsdes
Han, J., and Long, J · 2020
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