Splitting algorithm and normed convergence for drawing the random Loewner curves

Recent advances in Schramm–Loewner evolution have driven increasing interest in non-standard Loewner flows. In this work, we propose a novel splitting algorithm to simulate random Loewner curves with rigorous convergence analysis in sup-norm and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mi>p</mml:mi> </mml:msup> </mml:math> . The algorithm is further extended to explore fractional Schramm–Loewner evolution (SLE), driven by fractional Brownian motion, and noise-reinforced SLE, incorporating the effect on long-term memory. These exploratory and numerical extensions enable theoretical predictions on fractal dimensions and other statistical phenomena, providing new insights into such dynamics and opening directions for future research.

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Publication Details

Journal
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
Published
2025-08-01
DOI
https://doi.org/10.1098/rspa.2024.0929
Primary Topic
Mathematical Dynamics and Fractals
Type
article
Field-Weighted Citation Impact
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article

Splitting algorithm and normed convergence for drawing the random Loewner curves

Vlad Margarint, Jiaming Chen
Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
Mathematical Dynamics and Fractals
article

Splitting algorithm and normed convergence for drawing the random Loewner curves

Vlad Margarint, Jiaming Chen
article en

Abstract

Recent advances in Schramm–Loewner evolution have driven increasing interest in non-standard Loewner flows. In this work, we propose a novel splitting algorithm to simulate random Loewner curves with rigorous convergence analysis in sup-norm and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mi>p</mml:mi> </mml:msup> </mml:math> . The algorithm is further extended to explore fractional Schramm–Loewner evolution (SLE), driven by fractional Brownian motion, and noise-reinforced SLE, incorporating the effect on long-term memory. These exploratory and numerical extensions enable theoretical predictions on fractal dimensions and other statistical phenomena, providing new insights into such dynamics and opening directions for future research.

Proceedings of the Royal Society A Mathematical Physical and Engineering SciencesVol. 481(2320)
University of North Carolina at Charlotte (US), New York University Shanghai (CN), New York University (US)
Openalex Percentile: Top 73%
Mathematical Dynamics and Fractals
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