On multilevel Picard numerical approximations for high-dimensional nonlinear parabolic partial differential equations and high-dimensional nonlinear backward stochastic differential equations

From MaRDI portal
Publication:2316188

DOI10.1007/s10915-018-00903-0zbMath1418.65149arXiv1708.03223OpenAlexW2743985642WikidataQ128298743 ScholiaQ128298743MaRDI QIDQ2316188

Arnulf Jentzen, Martin Hutzenthaler, Thomas Kruse, E. Weinan

Publication date: 26 July 2019

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1708.03223



Related Items

Convergence of a Spatial Semidiscretization for a Backward Semilinear Stochastic Parabolic Equation, Convergence of a Robust Deep FBSDE Method for Stochastic Control, A new efficient approximation scheme for solving high-dimensional semilinear PDEs: control variate method for deep BSDE solver, Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations, Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities, On the speed of convergence of Picard iterations of backward stochastic differential equations, Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions, Adaptive deep neural networks methods for high-dimensional partial differential equations, Numerical computation of probabilities for nonlinear SDEs in high dimension using Kolmogorov equation, Three ways to solve partial differential equations with neural networks — A review, Deep learning methods for partial differential equations and related parameter identification problems, Numerical solution of the modified and non-Newtonian Burgers equations by stochastic coded trees, Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes, The Effect of the Number of Neural Networks on Deep Learning Schemes for Solving High Dimensional Nonlinear Backward Stochastic Differential Equations, XVA in a multi-currency setting with stochastic foreign exchange rates, A deep learning approach to the probabilistic numerical solution of path-dependent partial differential equations, An extreme learning machine-based method for computational PDEs in higher dimensions, Numerical methods for backward stochastic differential equations: a survey, Stability of backward stochastic differential equations: the general Lipschitz case, Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing, Models and numerical methods for XVA pricing under mean reversion spreads in a multicurrency framework, A deep-genetic algorithm (deep-GA) approach for high-dimensional nonlinear parabolic partial differential equations, A fully nonlinear Feynman-Kac formula with derivatives of arbitrary orders, A deep branching solver for fully nonlinear partial differential equations, Deep learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations, Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions, Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks, Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations, An overview on deep learning-based approximation methods for partial differential equations, A numerical approach to Kolmogorov equation in high dimension based on Gaussian analysis, Nesting Monte Carlo for high-dimensional non-linear PDEs, A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations, Quintic B-spline collocation method to solve \(n\)-dimensional stochastic Itô-Volterra integral equations, Convergence of the deep BSDE method for coupled FBSDEs, Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations, Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations, Decoupling on the Wiener Space, Related Besov Spaces, and Applications to BSDEs, Unnamed Item, Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations, Random walk approximation of BSDEs with Hölder continuous terminal condition, High-order combined multi-step scheme for solving forward backward stochastic differential equations, On existence and uniqueness properties for solutions of stochastic fixed point equations, Tractability for Volterra problems of the second kind with convolution kernels, Deep Splitting Method for Parabolic PDEs, Deep backward schemes for high-dimensional nonlinear PDEs, Multilevel Picard iterations for solving smooth semilinear parabolic heat equations, Asymptotic expansion as prior knowledge in deep learning method for high dimensional BSDEs, Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations, Strong rates of convergence for a space-time discretization of the backward stochastic heat equation, and of a linear-quadratic control problem for the stochastic heat equation, Iterative multilevel particle approximation for McKean-Vlasov SDEs, High order one-step methods for backward stochastic differential equations via Itô-Taylor expansion, Mean square rate of convergence for random walk approximation of forward-backward SDEs, Pseudorandom vector generation using elliptic curves and applications to Wiener processes, Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning, McKean Feynman-Kac probabilistic representations of non-linear partial differential equations, On nonlinear Feynman–Kac formulas for viscosity solutions of semilinear parabolic partial differential equations, Approximation Error Analysis of Some Deep Backward Schemes for Nonlinear PDEs, Deep ReLU neural networks overcome the curse of dimensionality for partial integrodifferential equations



Cites Work