Buchumschlag

Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Paulin, Daniel, Whalley, Peter A.
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning Numerical Analysis Probability
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

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

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
von: Paulin, Daniel, et al.
Veröffentlicht: (2024)

Data-driven approximation of transfer operators for mean-field stochastic differential equations
von: Ioannou, Eirini, et al.
Veröffentlicht: (2025)

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

Theoretical guarantees for stochastic gradient sampling methods via Gaussian convolution inequalities
von: Paulin, Daniel, et al.
Veröffentlicht: (2026)

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

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

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

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

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
von: Neufeld, Ariel, et al.
Veröffentlicht: (2024)

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

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

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

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

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

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

Asymptotic error distribution of Mittag--Leffler Euler method for a fractional stochastic differential equation
von: Dai, Xinjie, et al.
Veröffentlicht: (2026)

A new class of splitting methods that preserve ergodicity and exponential integrability for stochastic Langevin equation
von: Chen, Chuchu, et al.
Veröffentlicht: (2024)

On Bellman equations for continuous-time policy evaluation I: discretization and approximation
von: Mou, Wenlong, et al.
Veröffentlicht: (2024)

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

Weak error estimates of Galerkin approximations for the stochastic Burgers equation driven by additive trace-class noise
von: Bréhier, Charles-Edouard, et al.
Veröffentlicht: (2024)

Pathwise convergence of a novel numerical scheme based on semi-implicit method for stochastic differential-algebraic equations with non-global Lipschitz coefficients
von: Tsafack, Guy, et al.
Veröffentlicht: (2025)

Random Reshuffling for Stochastic Gradient Langevin Dynamics
von: Shaw, Luke, et al.
Veröffentlicht: (2025)

Artificial Barriers for stochastic differential equations and for construction of boundary-preserving schemes
von: Ulander, Johan
Veröffentlicht: (2024)

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

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

Improved estimates for the sharp interface limit of the stochastic Cahn-Hilliard equation with space-time white noise
von: Baňas, Ľubomír, et al.
Veröffentlicht: (2023)

Numerical approximation of ergodic BSDEs using non linear Feynman-Kac formulas
von: Gobet, Emmanuel, et al.
Veröffentlicht: (2024)

Long-time dynamics of stochastic wave equation with dissipative damping and its full discretization: exponential ergodicity and strong law of large numbers
von: Cai, Meng, et al.
Veröffentlicht: (2024)

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

A tamed-adaptive Milstein scheme for stochastic differential equations with low regularity coefficients
von: Vu, Thi-Huong, et al.
Veröffentlicht: (2024)

Exponential Euler method for stiff stochastic differential equations with additive fractional Brownian noise
von: Kamrani, Minoo, et al.
Veröffentlicht: (2023)

Analysis of kinetic Langevin Monte Carlo under the stochastic exponential Euler discretization from underdamped all the way to overdamped
von: Kim, Kyurae, et al.
Veröffentlicht: (2025)

Neural variance reduction for stochastic differential equations
von: Hinds, P. D., et al.
Veröffentlicht: (2022)

Rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of gradient-dependent semilinear heat equations
von: Neufeld, Ariel, et al.
Veröffentlicht: (2024)

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

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

Strong convergence rates for full-discrete approximations of the stochastic Allen-Cahn equations on 2D torus
von: Ma, Ting, et al.
Veröffentlicht: (2024)

Computing the invariant distribution of McKean-Vlasov SDEs by ergodic simulation
von: Chassagneux, Jean-François, et al.
Veröffentlicht: (2024)

Parameter-related strong convergence rates of Euler-type methods for time-changed stochastic differential equations
von: Zuo, Ruchun
Veröffentlicht: (2025)