Infinite-dimensional stochastic differential equations arising from Airy random point fields

The Airy $$_{\beta }$$ random point fields ( $$ \beta = 1,2,4$$ ) are random point fields emerging as the soft-edge scaling limits of eigenvalues of Gaussian random matrices. We construct the unlabeled diffusion reversible with respect to the Airy $$_{\beta }$$ random point field for each $$ \beta = 1,2,4$$ . We identify the infinite-dimensional stochastic differential equations (ISDEs) describing the labeled stochastic dynamics for the unlabeled diffusion mentioned above. We prove the existence and pathwise uniqueness of strong solutions of these ISDEs. Furthermore, the solution of the ISDE is the limit of the solutions of the stochastic differential equations describing the dynamics of the N-particle system in the soft-edge limit. We thus establish the construction of the stochastic dynamics whose unlabeled dynamics are reversible with respect to the Airy random point fields. When $$ \beta =2 $$ , the solution is equal to the stochastic dynamics defined by the space-time correlation functions obtained by Prähofer–Spohn, Johansson, Katori–Tanemura, and Corwin–Hammond, among others. We develop a new method whereby these ISDEs have unique, strong solutions. We expect that our approach is valid for other soft-edge scaling limits of stochastic dynamics arising from the random matrix theory.

Authors

Institutions

Publication Details

Journal
Stochastic Partial Differential Equations Analysis and Computations
Published
2024-12-13
DOI
https://doi.org/10.1007/s40072-024-00344-x
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Infinite-dimensional stochastic differential equations arising from Airy random point fields

Hirofumi Osada, Hideki Tanemura
Stochastic Partial Differential Equations Analysis and Computations
Random Matrices and Applications
article

Infinite-dimensional stochastic differential equations arising from Airy random point fields

Hirofumi Osada, Hideki Tanemura
article en

Abstract

The Airy $$_{\beta }$$ random point fields ( $$ \beta = 1,2,4$$ ) are random point fields emerging as the soft-edge scaling limits of eigenvalues of Gaussian random matrices. We construct the unlabeled diffusion reversible with respect to the Airy $$_{\beta }$$ random point field for each $$ \beta = 1,2,4$$ . We identify the infinite-dimensional stochastic differential equations (ISDEs) describing the labeled stochastic dynamics for the unlabeled diffusion mentioned above. We prove the existence and pathwise uniqueness of strong solutions of these ISDEs. Furthermore, the solution of the ISDE is the limit of the solutions of the stochastic differential equations describing the dynamics of the N-particle system in the soft-edge limit. We thus establish the construction of the stochastic dynamics whose unlabeled dynamics are reversible with respect to the Airy random point fields. When $$ \beta =2 $$ , the solution is equal to the stochastic dynamics defined by the space-time correlation functions obtained by Prähofer–Spohn, Johansson, Katori–Tanemura, and Corwin–Hammond, among others. We develop a new method whereby these ISDEs have unique, strong solutions. We expect that our approach is valid for other soft-edge scaling limits of stochastic dynamics arising from the random matrix theory.

Stochastic Partial Differential Equations Analysis and ComputationsVol. 13(2)
Chubu University (JP), Keio University (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 77%
Random Matrices and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.