:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Neufeld, Ariel, Nguyen, Tuan Anh
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis Probability
Online Access:	https://arxiv.org/abs/2403.09200
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities
by: Neufeld, Ariel, et al.
Published: (2023)

Rectified deep neural networks overcome the curse of dimensionality when approximating solutions of McKean--Vlasov stochastic differential equations
by: Neufeld, Ariel, et al.
Published: (2023)

Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations
by: Neufeld, Ariel, et al.
Published: (2023)

Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
by: Neufeld, Ariel, et al.
Published: (2024)

Multilevel Picard approximations overcome the curse of dimensionality when approximating semilinear heat equations with gradient-dependent nonlinearities in $L^p$-sense
by: Nguyen, Tuan Anh
Published: (2024)

Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion
by: Neufeld, Ariel, et al.
Published: (2025)

Multilevel Picard algorithm for general semilinear parabolic PDEs with gradient-dependent nonlinearities
by: Neufeld, Ariel, et al.
Published: (2023)

Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis
by: Neufeld, Ariel, et al.
Published: (2022)

Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
by: Neufeld, Ariel, et al.
Published: (2024)

Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
by: Liang, Luxu, et al.
Published: (2024)

Space-time deep neural network approximations for high-dimensional partial differential equations
by: Hornung, Fabian, et al.
Published: (2020)

Feasible approximation of matching equilibria for large-scale matching for teams problems
by: Neufeld, Ariel, et al.
Published: (2023)

Full history recursive multilevel Picard approximations suffer from the curse of dimensionality for the Hamilton-Jacobi-Bellman equation of a stochastic control problem
by: Hutzenthaler, Martin, et al.
Published: (2025)

Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints
by: Neufeld, Ariel, et al.
Published: (2022)

Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures
by: Neufeld, Ariel, et al.
Published: (2022)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for space-time solutions of semilinear partial differential equations
by: Ackermann, Julia, et al.
Published: (2024)

Bounds on the approximation error for deep neural networks applied to dispersive models: Nonlinear waves
by: Muñoz, Claudio, et al.
Published: (2024)

Boundary-preserving weak approximation for some semilinear stochastic partial differential equations
by: Ulander, Johan
Published: (2024)

Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
by: Neufeld, Ariel, et al.
Published: (2024)

Numerical approximation of the stochastic heat equation with a distributional reaction term
by: Goudenège, Ludovic, et al.
Published: (2024)

Explicit numerical approximations for McKean-Vlasov stochastic differential equations in finite and infinite time
by: Cui, Yuanping, et al.
Published: (2024)

Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)

Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis
by: Bartl, Daniel, et al.
Published: (2024)

Provably convergent stochastic fixed-point algorithm for free-support Wasserstein barycenter of continuous non-parametric measures
by: Chen, Zeyi, et al.
Published: (2025)

Finite element approximations of the stochastic Benjamin-Bona-Mahony equation with multiplicative noise
by: Nguyen, Hung D., et al.
Published: (2026)

The law of iterated logarithm for numerical approximation of time-homogeneous Markov process
by: Chen, Chuchu, et al.
Published: (2025)

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
by: Paulin, Daniel, et al.
Published: (2024)

Convergence rate of Euler-Maruyama scheme for McKean-Vlasov SDEs with density-dependent drift
by: Le, Anh-Dung
Published: (2024)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense
by: Ackermann, Julia, et al.
Published: (2023)

Numerical approximation of McKean-Vlasov SDEs via stochastic gradient descent
by: Agarwal, Ankush, et al.
Published: (2023)

Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation
by: Jentzen, Arnulf, et al.
Published: (2021)

Taming singular stochastic differential equations: A numerical method
by: Lê, Khoa, et al.
Published: (2021)

On the convergence of adaptive approximations for stochastic differential equations
by: Foster, James, et al.
Published: (2023)

From Monte Carlo to neural networks approximations of boundary value problems
by: Beznea, Lucian, et al.
Published: (2022)

Existence, uniqueness, and numerical approximations for stochastic Burgers equations
by: Mazzonetto, Sara, et al.
Published: (2019)

Wellposedness, exponential ergodicity and numerical approximation of fully super-linear McKean--Vlasov SDEs and associated particle systems
by: Chen, Xingyuan, et al.
Published: (2023)

Weak approximation of stochastic differential equations with sticky boundary conditions
by: Sharma, Akash
Published: (2025)

Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting
by: Neufeld, Ariel, et al.
Published: (2022)

Mean-square exponential stability of exact and numerical solutions for neutral stochastic delay differential equations with Markovian switching
by: Yang, Jina, et al.
Published: (2025)