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We study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process.
R. Tyrrell Rockafellar · 1970
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Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
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Backward stochastic differential equations with constraints on the gains-process
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Numerical simulation of bsdes using empirical regression methods: theory and practice
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Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, and Ariel Neufeld · 2019
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Étienne Pardoux · 1998
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Shige Peng · 1999
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Some machine learning schemes for high-dimensional nonlinear pdes
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Machine learning for semi linear pdes
Quentin Chan-Wai-Nam, Joseph Mikael, and Xavier Warin · 2019
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A numerical probabilistic scheme for super-replication with convex constraints on the delta
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