From large deviations to Wasserstein gradient flows in multiple dimensions

被引:19
|
作者
Erbar, Matthias [1 ]
Maas, Jan [2 ]
Renger, D. R. Michiel [3 ]
机构
[1] Univ Bonn, Bonn, Germany
[2] Inst Sci & Technol Austria IST Austria, Klosterneuburg, Austria
[3] WIAS Berlin, Berlin, Germany
关键词
Large deviations; gradient flows; Wasserstein metric; Gamma-convergence; PRINCIPLE;
D O I
10.1214/ECP.v20-4315
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We study the large deviation rate functional for the empirical distribution of independent Brownian particles with drift. In one dimension, it has been shown by Adams, Dirr, Peletier and Zimmer that this functional is asymptotically equivalent (in the sense of Gamma-convergence as the time-step goes to zero) to the Jordan-Kinderlehrer-Otto functional arising in the Wasserstein gradient flow structure of the Fokker-Planck equation. In higher dimensions, part of this statement (the lower bound) has been recently proved by Duong, Laschos and Renger, but the upper bound remained open, since their proof of the upper bound relies on regularity properties of optimal transport maps that are restricted to one dimension. In this note we present a new proof of the upper bound, thereby generalising the result of Adams, Dirr, Peletier and Zimmer to arbitrary dimensions.
引用
收藏
页码:1 / 12
页数:12
相关论文
共 50 条
  • [21] Fisher information regularization schemes for Wasserstein gradient flows
    Li, Wuchen
    Lu, Jianfeng
    Wang, Li
    JOURNAL OF COMPUTATIONAL PHYSICS, 2020, 416
  • [22] Modeling of Political Systems using Wasserstein Gradient Flows
    Lanzetti, Nicolas
    Hajar, Joudi
    Dorfler, Florian
    2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), 2022, : 364 - 369
  • [23] Gradient flows in three dimensions
    I. Jack
    D. R. T. Jones
    C. Poole
    Journal of High Energy Physics, 2015
  • [24] Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies
    Ziesche, Hanna
    Rozo, Leonel
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [25] Gradient flows in three dimensions
    Jack, I.
    Jones, D. R. T.
    Poole, C.
    JOURNAL OF HIGH ENERGY PHYSICS, 2015, (09):
  • [26] Large deviations for stochastic flows and their applications
    Gao, FQ
    Ren, JG
    SCIENCE IN CHINA SERIES A-MATHEMATICS, 2001, 44 (08): : 1016 - 1033
  • [27] Large deviations for stochastic flows and their applications
    Gao, F.
    Ren, J.
    2001, Science in China Press (44): : 1016 - 1033
  • [28] Large deviations for stochastic flows of diffeomorphisms
    Budhiraja, Amarjit
    Dupuis, Paul
    Maroulas, Vasileios
    BERNOULLI, 2010, 16 (01) : 234 - 257
  • [29] LARGE DEVIATIONS AND STOCHASTIC FLOWS OF DIFFEOMORPHISMS
    BAXENDALE, PH
    STROOCK, DW
    PROBABILITY THEORY AND RELATED FIELDS, 1988, 80 (02) : 169 - 215
  • [30] Sharp Large Deviations for Hyperbolic Flows
    Petkov, Vesselin
    Stoyanov, Luchezar
    ANNALES HENRI POINCARE, 2020, 21 (12): : 3791 - 3834