Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 31 min ago
Peer-reviewedCC BY 4.0Mathematics

Stratonovich SDEs in High Dimensions: Weak Convergence Approach to Generalized Trivial Coupling

  1. 1Department of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi Arabia
  2. 2Department of Statistics and Operations Research, College of Science, Qassim University, Buraydah 51452, Saudi Arabia

Abstract

This paper uses a generalized trivial coupling method to study the weak convergence of Stratonovich stochastic differential equations (SDEs) in high-dimensional environments. In contrast to standard methods that depend on the invertibility of the diffusion matrix or are restricted to low dimensions, our methodology deals with degenerate and non-invertible systems in arbitrary dimensions. We develop a theoretical framework for weak approximation, establishing the weak order of convergence under standard regularity assumptions. The method is integrated with multi-level Monte Carlo (MLMC) techniques to achieve optimal computational complexity O(ϵ−2) for mean square error ϵ2. We provide a detailed analysis of the variance decay, proving that Vℓ=O(δℓ2) for the coupled estimator, which leads to optimal complexity. Representative numerical tests on a 4-dimensional non-invertible system validate the theoretical results, proving the usefulness of the suggested approach. The numerical results demonstrate the computing benefits of the multi-level technique in high dimensions and validate the theoretical convergence rates. By offering both theoretical insights and useful computational methods for high-dimensional stochastic systems, this work advances the expanding field of computational stochastic analysis.

Related

  1. Reversible Stratonovich perturbations for accelerating convergence to equilibrium of Langevin dynamics
    Guillaume Louis, Grigorios A. Pavliotis, Gilles Vilmart
    arXivPreprintarXiv licenceFull textNumerical analysis & scientific computingProbabilityarXiv
  2. Cluster Representation of Renormalization Group Transformations and a Rigorous Proof for Convergence of the RG-Flow of the Ising Model to Trivial Fixed Points away from Criticality
    Fabio Arz
    arXivPreprintCC BY 4.0Full textMathematical physicsHigh-energy & particle physicsarXiv
  3. Convergence of Independence Metropolis-Hastings Algorithms in High Dimensions: Iteration Lengths, Cutoff Profiles, and Diagnostic Unreliability
    Austin Brown
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  4. Generalized Discrete Fractal Dimensions of Graph Families
    Jiantao Liu, Cheng Zeng, Yumei Xue +1
    arXivPreprintCC BY 4.0Full textCombinatorics & discrete mathematicsGeometry & topologyarXiv
  5. Solvable Algebraic Generalized Ricci Solitons in low dimensions
    Lorenzo Mucciante
    arXivPreprintarXiv licenceFull textGeometry & topologyarXiv
BibTeX
@article{alnafisah2026stratonovich,
  title = {{Stratonovich SDEs in High Dimensions: Weak Convergence Approach to Generalized Trivial Coupling}},
  author = {Yousef Alnafisah and M. A. Sohaly},
  journal = {Mathematics},
  year = {2026},
  month = oct,
  doi = {10.3390/math14203760},
  url = {https://www.mdpi.com/2227-7390/14/20/3760},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Stratonovich SDEs in High Dimensions: Weak Convergence Approach to Generalized Trivial Coupling
AU  - Yousef Alnafisah
AU  - M. A. Sohaly
PY  - 2026
DA  - 2026/10/16
JO  - Mathematics
DO  - 10.3390/math14203760
UR  - https://www.mdpi.com/2227-7390/14/20/3760
AB  - This paper uses a generalized trivial coupling method to study the weak convergence of Stratonovich stochastic differential equations (SDEs) in high-dimensional environments. In contrast to standard methods that depend on the invertibility of the diffusion matrix or are restricted to low dimensions, our methodology deals with degenerate and non-invertible systems in arbitrary dimensions. We develop a theoretical framework for weak approximation, establishing the weak order of convergence under standard regularity assumptions. The method is integrated with multi-level Monte Carlo (MLMC) techniques to achieve optimal computational complexity O(ϵ−2) for mean square error ϵ2. We provide a detailed analysis of the variance decay, proving that Vℓ=O(δℓ2) for the coupled estimator, which leads to optimal complexity. Representative numerical tests on a 4-dimensional non-invertible system validate the theoretical results, proving the usefulness of the suggested approach. The numerical results demonstrate the computing benefits of the multi-level technique in high dimensions and validate the theoretical convergence rates. By offering both theoretical insights and useful computational methods for high-dimensional stochastic systems, this work advances the expanding field of computational stochastic analysis.
SN  - 2227-7390
ER  -