Pages that link to "Item:Q2063953"
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The following pages link to Multilevel Picard iterations for solving smooth semilinear parabolic heat equations (Q2063953):
Displaying 27 items.
- Pseudorandom vector generation using elliptic curves and applications to Wiener processes (Q2101189) (← links)
- Strong convergence rate of Euler-Maruyama approximations in temporal-spatial Hölder-norms (Q2146346) (← links)
- Extensions of the deep Galerkin method (Q2148058) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115) (← links)
- On the speed of convergence of Picard iterations of backward stochastic differential equations (Q2165738) (← links)
- Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions (Q2165859) (← links)
- A numerical approach to Kolmogorov equation in high dimension based on Gaussian analysis (Q2208270) (← links)
- A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations (Q2216499) (← 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 for semi linear PDEs (Q2316193) (← links)
- Numerical computation of probabilities for nonlinear SDEs in high dimension using Kolmogorov equation (Q2673974) (← links)
- Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← 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)
- Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
- A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations (Q5889064) (← 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)
- Boundary-safe PINNs extension: application to non-linear parabolic PDEs in counterparty credit risk (Q6157931) (← links)
- Numerical methods for backward stochastic differential equations: a survey (Q6158181) (← links)
- Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← 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)
- Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions (Q6204733) (← links)
- The Calderón's problem via DeepONets (Q6570540) (← links)
- Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations (Q6645961) (← links)
