Pages that link to "Item:Q1731144"

The following pages link to Branching diffusion representation of semilinear PDEs and Monte Carlo approximation (Q1731144):

Displaying 48 items.

• Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations (Q681281) (← links)
• Numerical solution for the non-linear Dirichlet problem of a branching process (Q1683731) (← links)
• Numerical approximation of BSDEs using local polynomial drivers and branching processes (Q1691497) (← links)
• Unbiased simulation of stochastic differential equations (Q1704137) (← links)
• Nesting Monte Carlo for high-dimensional non-linear PDEs (Q1713854) (← links)
• Monte-Carlo algorithms for a forward Feynman-Kac-type representation for semilinear nonconservative partial differential equations (Q1746430) (← 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)
• Multilevel Picard iterations for solving smooth semilinear parabolic heat equations (Q2063953) (← links)
• Numerical evaluation of ODE solutions by Monte Carlo enumeration of Butcher series (Q2098780) (← links)
• McKean Feynman-Kac probabilistic representations of non-linear partial differential equations (Q2107414) (← links)
• Existence and probabilistic representation of the solutions of semilinear parabolic PDEs with fractional Laplacians (Q2158592) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115) (← links)
• Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions (Q2165859) (← links)
• Branching diffusion representation of semi-linear elliptic PDEs and estimation using Monte Carlo method (Q2186658) (← links)
• Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks (Q2201474) (← links)
• Convergence of the deep BSDE method for coupled FBSDEs (Q2223111) (← links)
• Branching diffusion representation for nonlinear Cauchy problems and Monte Carlo approximation (Q2240887) (← links)
• On multilevel Picard numerical approximations for high-dimensional nonlinear parabolic partial differential equations and high-dimensional nonlinear backward stochastic differential equations (Q2316188) (← links)
• Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations (Q2327815) (← links)
• A fully nonlinear Feynman-Kac formula with derivatives of arbitrary orders (Q2690084) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations (Q2694433) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Neural network-based variational methods for solving quadratic porous medium equations in high dimensions (Q2699489) (← links)
• Deep Splitting Method for Parabolic PDEs (Q4958922) (← links)
• Deep backward schemes for high-dimensional nonlinear PDEs (Q4960067) (← links)
• Monte-Carlo methods for the pricing of American options: a semilinear BSDE point of view (Q4967878) (← links)
• Numerical approximation of general Lipschitz BSDEs with branching processes (Q4967879) (← links)
• An Explicit Second Order Scheme for Decoupled Anticipated Forward Backward Stochastic Differential Equations (Q4986617) (← links)
• Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
• Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs (Q5021399) (← links)
• An Unbiased Itô Type Stochastic Representation for Transport PDEs: A Toy Example (Q5038297) (← links)
• Forward Feynman-Kac type representation for semilinear non-conservative partial differential equations (Q5087049) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations (Q5161194) (← links)
• Optimal Market Making with Persistent Order Flow (Q5162846) (← links)
• Unbiased Monte Carlo estimate of stochastic differential equations expectations (Q5350276) (← links)
• Numerical Solution of the Incompressible Navier-Stokes Equation by a Deep Branching Algorithm (Q6049610) (← links)
• Monotone methods in counterparty risk models with nonlinear Black-Scholes-type equations (Q6055837) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• Numerical solution of the modified and non-Newtonian Burgers equations by stochastic coded trees (Q6072375) (← links)
• XVA in a multi-currency setting with stochastic foreign exchange rates (Q6102925) (← links)
• Numerical methods for backward stochastic differential equations: a survey (Q6158181) (← links)
• Stability of backward stochastic differential equations: the general Lipschitz case (Q6165206) (← links)
• Existence of solutions for nonlinear elliptic PDEs with fractional Laplacians on open balls (Q6176257) (← links)
• Stochastic solutions and singular partial differential equations (Q6177833) (← links)
• A deep branching solver for fully nonlinear partial differential equations (Q6196609) (← links)
• Iterative schemes for probabilistic domain decomposition (Q6491443) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
• Analysis of deep Ritz methods for semilinear elliptic equations (Q6662390) (← links)