Pages that link to "Item:Q2162115"
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The following pages link to Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115):
Displaying 12 items.
- Nesting Monte Carlo for high-dimensional non-linear PDEs (Q1713854) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations (Q2025321) (← links)
- On the speed of convergence of Picard iterations of backward stochastic differential equations (Q2165738) (← links)
- Active learning based sampling for high-dimensional nonlinear partial differential equations (Q2683063) (← links)
- A fully nonlinear Feynman-Kac formula with derivatives of arbitrary orders (Q2690084) (← links)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- Numerical solution of the modified and non-Newtonian Burgers equations by stochastic coded trees (Q6072375) (← links)
- Numerical methods for backward stochastic differential equations: a survey (Q6158181) (← links)
- A deep branching solver for fully nonlinear partial differential equations (Q6196609) (← links)
- Deep learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations (Q6201366) (← links)
- Statistical variational data assimilation (Q6643561) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
