Cover Image

Data-driven computation for periodic stochastic differential equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yao, Sun, Jiatong
Format:	Preprint
Published:	2025
Subjects:	Numerical Analysis 65C99, 68T07, 60H10
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators
by: Lozano, Pere Díaz, et al.
Published: (2024)

Uniform in time convergence of numerical schemes for stochastic differential equations via Strong Exponential stability: Euler methods, Split-Step and Tamed Schemes
by: Angeli, Letizia, et al.
Published: (2023)

Error analysis for learning fractional stochastic differential equations with applications in neural approximations
by: Dehshiri, Mahdi, et al.
Published: (2026)

Score-based constrained generative modeling via Langevin diffusions with boundary conditions
by: Nordenhög, Adam, et al.
Published: (2025)

A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

Non-asymptotic uniform in time error bounds for new and old numerical schemes for SPDEs
by: Huang, Can, et al.
Published: (2026)

An Euler scheme for BSDEs via the Wiener chaos decomposition
by: Lozano, Pere Díaz, et al.
Published: (2025)

Functional SDE approximation inspired by a deep operator network architecture
by: Eigel, Martin, et al.
Published: (2024)

Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations
by: Xiao, Jichang, et al.
Published: (2023)

Order-one explicit approximations of random periodic solutions of semi-linear SDEs with multiplicative noise
by: Guo, Yujia, et al.
Published: (2025)

Convergence in Density of Splitting AVF Scheme for Stochastic Langevin Equation
by: Cui, Jianbo, et al.
Published: (2019)

The Stochastic TR-BDF2 Scheme of Order 2
by: Caraballo, Tomás, et al.
Published: (2026)

$α$-scaled strong convergence of stochastic theta method for stochastic differential equations driven by time-changed Lévy noise beyond Lipschitz continuity
by: Chen, Jingwei
Published: (2025)

Long-time behavior of exact and numerical solutions of stochastic evolution equations on the sphere
by: Cohen, David, et al.
Published: (2026)

Convergence proofs and strong error bounds for forward-backward stochastic differential equations using neural network simulations
by: Sheridan-Methven, Oliver
Published: (2024)

Nonclassical symmetries of polynomial equations and test problems with parameters for computer algebra systems
by: Shingareva, Inna K., et al.
Published: (2026)

Strong convergence and Mittag-Leffler stability of stochastic theta method for time-changed stochastic differential equations
by: Chen, Jingwei, et al.
Published: (2025)

Error bounds for Physics Informed Neural Networks in Nonlinear Schrödinger equations placed on unbounded domains
by: Alejo, Miguel Á., et al.
Published: (2024)

Lawson schemes for highly oscillatory stochastic differential equations and conservation of invariants
by: Debrabant, Kristian, et al.
Published: (2019)

Strong convergence rates of stochastic theta methods for index 1 stochastic differential algebraic equations under non-globally Lipschitz conditions
by: Chen, Lin, et al.
Published: (2025)

A projected Euler Method for Random Periodic Solutions of Semi-linear SDEs with non-globally Lipschitz coefficients
by: Guo, Yujia, et al.
Published: (2024)

Error estimates of asymptotic-preserving neural networks in approximating stochastic linearized Boltzmann equation
by: Wan, Jiayu, et al.
Published: (2025)

Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems
by: Caldana, Matteo, et al.
Published: (2024)

Generative Modelling of Lévy Area for High Order SDE Simulation
by: Jelinčič, Andraž, et al.
Published: (2023)

On splitting strategies for the numerical solution of stochastic delay differential equations with correlated noises
by: Kelly, Cónall, et al.
Published: (2026)

Strong solution of stochastic differential equations with discontinuous and unbounded coefficients
by: Hu, Yaozhong, et al.
Published: (2023)

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)

On the complexity of strong approximation of stochastic differential equations with a non-Lipschitz drift coefficient
by: Müller-Gronbach, T., et al.
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)

Stochastic conformal integrators for linearly damped stochastic Poisson systems
by: Bréhier, Charles-Edouard, et al.
Published: (2025)

Data-driven structure-preserving model reduction for stochastic Hamiltonian systems
by: Tyranowski, Tomasz M.
Published: (2022)

Preserving invariant domains and strong approximation of stochastic differential equations
by: Erdogan, Utku, et al.
Published: (2025)

Prediction of discretization of online GMsFEM using deep learning for Richards equation
by: Spiridonov, Denis, et al.
Published: (2024)

All Equalities Are Equal, but Some Are More Equal Than Others: The Effect of Implementation Aliasing on the Numerical Solution to Conservation Equations
by: Trojak, Will, et al.
Published: (2019)

Dimension reduction for large-scale stochastic systems with non-zero initial states and controlled diffusion
by: Redmann, Martin
Published: (2024)

A forward scheme with machine learning for forward-backward SDEs with jumps by decoupling jumps
by: Kawai, Reiichiro, et al.
Published: (2024)

Enforcing boundary conditions for physics-informed neural operators
by: Göschel, Niklas, et al.
Published: (2025)

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
by: Park, Youngjae, et al.
Published: (2026)

An $L^0$-approach to stochastic evolution equations
by: Auestad, Øyvind Stormark
Published: (2025)