An encoded FBSNN uses tensor encoding of inputs as images and CNN processing to approximate high-dimensional BSDEs more efficiently than vanilla FBSNN.
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Pith papers citing it
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math.NA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DBR reformulates backward losses via conditional expectations and Monte Carlo averaging to create smoother training targets for deep neural network solvers of high-dimensional nonlinear PDEs, yielding competitive benchmarks and half-order convergence under stated assumptions.
citing papers explorer
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Encoded Forward Backward Stochastic Neural Network for High-Dimensional Backward Stochastic Differential Equations and Parabolic Partial Differential Equations
An encoded FBSNN uses tensor encoding of inputs as images and CNN processing to approximate high-dimensional BSDEs more efficiently than vanilla FBSNN.
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A deep backward regression-based scheme for high-dimensional nonlinear partial differential equations
DBR reformulates backward losses via conditional expectations and Monte Carlo averaging to create smoother training targets for deep neural network solvers of high-dimensional nonlinear PDEs, yielding competitive benchmarks and half-order convergence under stated assumptions.