Stochastic Wasserstein Hamiltonian Flows

被引:5
|
作者
Cui, Jianbo [1 ]
Liu, Shu [2 ]
Zhou, Haomin [3 ]
机构
[1] Hong Kong Polytech Univ, Dept Appl Math, Kowloon, Hong Kong, Peoples R China
[2] UCLA, Dept Math, Los Angeles, CA 90095 USA
[3] Georgia Tech, Sch Math, Atlanta, GA 30332 USA
关键词
Stochastic Hamiltonian flow; Density manifold; Wong-Zakai approximation; SCHRODINGER-EQUATION; OPTIMAL TRANSPORT; APPROXIMATIONS; CONVERGENCE; DYNAMICS; SPACE;
D O I
10.1007/s10884-023-10264-4
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we study the stochastic Hamiltonian flow in Wasserstein manifold, the probability density space equipped with L-2-Wasserstein metric tensor, via the Wong-Zakai approximation. We begin our investigation by showing that the stochastic Euler-Lagrange equation, regardless it is deduced from either the variational principle or particle dynamics, can be interpreted as the stochastic kinetic Hamiltonian flows in Wasserstein manifold. We further propose a novel variational formulation to derive more general stochastic Wasserstein Hamiltonian flows, and demonstrate that this new formulation is applicable to various systems including the stochastic Schrodinger equation, Schrodinger equation with random dispersion, and Schrodinger bridge problem with common noise.
引用
收藏
页码:3885 / 3921
页数:37
相关论文
共 50 条
  • [21] Primal Dual Methods for Wasserstein Gradient Flows
    Carrillo, Jose A.
    Craig, Katy
    Wang, Li
    Wei, Chaozhen
    FOUNDATIONS OF COMPUTATIONAL MATHEMATICS, 2022, 22 (02) : 389 - 443
  • [22] {Euclidean, metric, and Wasserstein} gradient flows: an overview
    Santambrogio, Filippo
    BULLETIN OF MATHEMATICAL SCIENCES, 2017, 7 (01) : 87 - 154
  • [23] Primal Dual Methods for Wasserstein Gradient Flows
    José A. Carrillo
    Katy Craig
    Li Wang
    Chaozhen Wei
    Foundations of Computational Mathematics, 2022, 22 : 389 - 443
  • [24] Understanding MCMC Dynamics as Flows on the Wasserstein Space
    Liu, Chang
    Zhuo, Jingwei
    Zhu, Jun
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97
  • [25] Variational inference via Wasserstein gradient flows
    Lambert, Marc
    Chewi, Sinho
    Bach, Francis
    Bonnabel, Silvere
    Rigollet, Philippe
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
  • [26] Stochastic surrogate Hamiltonian
    Katz, Gil
    Gelman, David
    Ratner, Mark A.
    Kosloff, Ronnie
    JOURNAL OF CHEMICAL PHYSICS, 2008, 129 (03):
  • [27] Approximating stochastic biochemical processes with Wasserstein pseudometrics
    Thorsley, D.
    Klavins, E.
    IET SYSTEMS BIOLOGY, 2010, 4 (03) : 193 - 211
  • [28] ON THE STOCHASTIC CONVERGENCE OF REPRESENTATIONS BASED ON WASSERSTEIN METRICS
    TUERO, A
    ANNALS OF PROBABILITY, 1993, 21 (01): : 72 - 85
  • [29] Wasserstein Weight Estimation for Stochastic Petri Nets
    Brockhoff, Tobias
    Uysal, Merih Seran
    van der Aalst, Wil M. P.
    2024 6TH INTERNATIONAL CONFERENCE ON PROCESS MINING, ICPM, 2024, : 81 - 88
  • [30] Backward and forward Wasserstein projections in stochastic order
    Kim, Young-Heon
    Ruan, Yuanlong
    JOURNAL OF FUNCTIONAL ANALYSIS, 2024, 286 (02)