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An explicit Euler method for McKean-Vlasov SDEs driven by fractional Brownian motion

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arxiv 2209.04574 v1 pith:2P2ZSWGW submitted 2022-09-10 math.NA cs.NAmath.PR

Jie He , Shuaibin Gao , Weijun Zhan , Qian Guo This is my paper · ORCID
classification math.NAcs.NAmath.PR
keywords browniandriveneulerfractionalmckean-vlasovmethodmotionbounds
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abstract

In this paper, we establish the theory of chaos propagation and propose an Euler-Maruyama scheme for McKean-Vlasov stochastic differential equations driven by fractional Brownian motion with Hurst exponent $H \in (0,1)$. Meanwhile, upper bounds for errors in the Euler method is obtained. A numerical example is demonstrated to verify the theoretical results.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Solving McKean-Vlasov Equation by deep learning particle method

    math.NA 2025-01 reject novelty 6.0 of 10

    A deep learning particle method is proposed that represents McKean-Vlasov SDE solutions as neural functions of Brownian motion and minimizes coefficient residuals, but representation is not proved to be expressive enough.

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