PENG Linyu

写真a

Affiliation

Faculty of Science and Technology, Department of Mechanical Engineering (Yagami)

Position

Associate Professor

Related Websites

External Links

Career 【 Display / hide

  • 2013.10
    -
    2015.03

    Waseda University, Junior Researcher

  • 2015.04
    -
    2017.03

    Waseda University , Assistant Professor

  • 2017.04
    -
    2020.03

    Waseda University, Assistant Professor

  • 2020.04
    -
    2023.03

    Keio University, Assistant Professor

  • 2023.04
    -
    Present

    Keio University , Associate Professor

Academic Background 【 Display / hide

  • 2004.09
    -
    2008.06

    Beijing Institute of Technology

    University, Graduated

  • 2008.09
    -
    2010.07

    Beijing Institute of Technology

    Graduate School, Completed, Master's course

  • 2010.10
    -
    2013.07

    University of Surrey

    Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide

  • PhD, University of Surrey, Coursework, 2013.09

 

Research Areas 【 Display / hide

  • Natural Science / Basic mathematics (Applied Mathematics)

  • Natural Science / Applied mathematics and statistics (Applied Mathematics)

Research Keywords 【 Display / hide

  • Symmetries and conservation laws

  • Geometric mechanics

  • Geometric integration

  • Information geometry

 

Books 【 Display / hide

  • Mathematical Foundations of Information Geometry

    H. Sun, L. Peng, Y. Cheng, D. Li, L. Jiu,, Science Press, Beijing, 2025.03

  • Paving the Way for 5G Through the Convergence of Wireless Systems

    X. Zhang, Y. Cao, L. Peng, J. Li, IGI Global Publisher, 2019

    Scope: Enhancing Mobile Data Offloading With In-Network Caching,  Contact page: 250-270

  • An Elementary Introduction to Information Geometry

    H. Sun, Z. Zhang, L. Peng, X. Duan, Science Press, Beijing, 2016.03

  • Object Recognition

    F Li, L Peng, H Sun, 2011

    Scope: Fibre Bundle Models and 3D Object Recognition,  Contact page: 317-332

Papers 【 Display / hide

  • Operator-based graph linear canonical transform

    Jian-Yi Chen, Bing-Zhao Li, Linyu Peng

    Journal of the Franklin Institute 362 ( 12 )  2025.08

    ISSN  00160032

     View Summary

    This paper proposes a novel graph linear canonical transform framework based on hyper-differential operators, referred to as OGLCT. First, the relationship between the graph fractional Fourier transform (GFrFT) and the graph Fourier transform (GFT) is analyzed, and hyper-differential operators associated with the GFT matrix are derived through solutions to the Sylvester equation. Using these operators, the GFrFT, graph scaling, and graph chirp modulation are defined, culminating in the formal definition of the OGLCT and its fundamental properties. Next, the implementation of the OGLCT is discussed, and parameter optimization techniques are explored. Finally, the application of the OGLCT for feature extraction in image processing is explored, and its effectiveness in image classification tasks is investigated. By adjusting the parameters of the OGLCT, it is demonstrated that flexible tuning significantly enhances model performance, enabling more effective and versatile applications.

  • The Effect of Accuracy of Initial Velocity Discretisations on Discrete Energy in Variational Integration

    M Gunji, Y Ono, L Peng

    Proceedings of the IUTAM Symposium on Nonlinear Dynamics for Design of … (Springer Cham)     88 - 98 2025.01

  • Euler--Poincar\'e reduction and the Kelvin--Noether theorem for discrete mechanical systems with advected parameters and additional dynamics

    Y Ono, S Fiori, L Peng

    arXiv preprint arXiv:2501.12940  2025

  • Reconstruction of Graph Signals on Complex Manifolds with Kernel Methods

    Y Zhang, L Peng, BZ Li

    arXiv preprint arXiv:2505.15202  2025

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Papers, etc., Registered in KOARA 【 Display / hide

Reviews, Commentaries, etc. 【 Display / hide

  • Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization

    Weizhi Gao, Zhichao Hou, Junqi Yin, Feiyi Wang, Linyu Peng, Xiaorui Liu

    arXiv preprint arXiv:2506.22463 (arXiv)   2025.06

     View Summary

    Diffusion models have emerged as powerful generative models, but their high
    computation cost in iterative sampling remains a significant bottleneck. In
    this work, we present an in-depth and insightful study of state-of-the-art
    acceleration techniques for diffusion models, including caching and
    quantization, revealing their limitations in computation error and generation
    quality. To break these limits, this work introduces Modulated Diffusion
    (MoDiff), an innovative, rigorous, and principled framework that accelerates
    generative modeling through modulated quantization and error compensation.
    MoDiff not only inherents the advantages of existing caching and quantization
    methods but also serves as a general framework to accelerate all diffusion
    models. The advantages of MoDiff are supported by solid theoretical insight and
    analysis. In addition, extensive experiments on CIFAR-10 and LSUN demonstrate
    that MoDiff significant reduces activation quantization from 8 bits to 3 bits
    without performance degradation in post-training quantization (PTQ). Our code
    implementation is available at https://github.com/WeizhiGao/MoDiff.

  • Stochastic multisymplectic PDEs and their structure-preserving numerical methods

    Ruiao Hu, Linyu Peng

    arXiv preprint arXiv:2501.16913 (arXiv)   2025.01

     View Summary

    We construct stochastic multisymplectic systems by considering a stochastic
    extension to the variational formulation of multisymplectic partial
    differential equations proposed in [Hydon, {\it Proc. R. Soc. A}, 461,
    1627--1637, 2005]. The stochastic variational principle implies the existence
    of stochastic $1$-form and $2$-form conservation laws, as well as conservation
    laws arising from continuous variational symmetries via a stochastic Noether's
    theorem. These results are the stochastic analogues of those found in
    deterministic variational principles. Furthermore, we develop stochastic
    structure-preserving collocation methods for this class of stochastic
    multisymplectic systems. These integrators possess a discrete analogue of the
    stochastic $2$-form conservation law and, in the case of linear systems, also
    guarantee discrete momentum conservation. The effectiveness of the proposed
    methods is demonstrated through their application to stochastic nonlinear
    Schr\"odinger equations featuring either stochastic transport or stochastic
    dispersion.

Presentations 【 Display / hide

  • Discrete Lagrangian multiforms on the difference variational bicomplex

    Linyu Peng

    BIRS Workshop Lagrangian Multiform Theory and Pluri-Lagrangian Systems, 

    2023.10

  • The modified formal variational formulation for general differential equations and applications

    Linyu Peng

    持続的環境エネルギー社会共創研究機構 研究所間交流会, 

    2023.09

  • Applications of Bures-Wasserstein geometry of HPD matrices to signal detection

    Y. Ono, L. Peng

    10th International Congress on Industrial and Applied Mathematics (ICIAM2023), 

    2023.08

  • A discretization of Dirac structures and Lagrange-Dirac dynamical systems

    H. Yoshimura, L. Peng

    10th International Congress on Industrial and Applied Mathematics (ICIAM2023), 

    2023.08

  • The influence of accuracy of initial values on the discrete energy in variational integrator

    M. Gunji, Y. Ono, L. Peng

    IUTAM Symposium on Nonlinear Dynamics for Design of Mechanical Systems across Different Length/Time Scales (IUTAM2023), 

    2023.07
    -
    2023.08

    Poster presentation

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Research Projects of Competitive Funds, etc. 【 Display / hide

  • Symmetry Methods for Discrete Equations and Their Applications

    2024.04
    -
    2028.03

    Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

     View Summary

    Symmetries have proven to be of great importance in various fields, owing to the versatile applications in elucidating solution properties to physical models. Many scholars made substantial contributions to the study of symmetry methods for discrete equations, giving rise to a plethora of subsequent research and applications. Despite these advancements, a multitude of unresolved questions continued to challenge the field. The primary objective of the current project is to tackle some of the unresolved questions concerning the symmetries of discrete equations and to explore their applications.

  • Multisymplectic Geometry and Geometric Numerical Integrator for Variational Problems

    2020.04
    -
    2024.03

    Keio University, Grants-in-Aid for Scientific Research, Linyu Peng, Grant-in-Aid for Early-Career Scientists, Research grant, Principal investigator

     View Summary

    Geometric integrator is among one of the most efficient numerical methods for differential equations. In this project, we establish a unified and systematical analogue for understanding both continuous and discrete multisymplectic structures of arbitrary order variational differential equations.

  • 複雑な流体現象のモデリング,マルチスケール構造の解明と数理解析

    2016.04
    -
    2019.03

    Waseda University, Grants-in-Aid for Scientific Research, Hiroaki Yoshimura, Grant-in-Aid for Scientific Research (B), Research grant, Coinvestigator(s)

     View Summary

    We have explored mathematical modeling of complex fluid phenomena, mathematical analysis of partial differential equations and stochastic differential equations associated to multi-scale phenomena as well as applications of nonlinear mechanics. For the mathematical modeling, we have studied a Lagrangian variational formulation of nonequilibrium thermodynamics, modeling of cloud cavitation and with experiments, elucidation of LCS (Lagrangian coherent structures) for Rayleigh-Benard convection as well as a stochastic variational formulation of single bubble dynamics. For the mathematical analysis, we have researched on the existence and uniqueness of Navier-Stokes equations for two-phase flows, stochastic KPZ equations and modified KdV equations. Further we have shown some applications of LCS analysis to space mission design.

 

Courses Taught 【 Display / hide

  • PROJECT LABORATORY IN MECHANICAL ENGINEERING

    2025

  • NONLINEAR DYNAMICS

    2025

  • LIBERAL ARTS AND SCIENCES SEMINAR (GIC) 1

    2025

  • LAGRANGIAN AND HAMILTONIAN DYNAMICS

    2025

  • INDEPENDENT STUDY ON SCIENCE FOR OPEN AND ENVIRONMENTAL SYSTEMS

    2025

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Memberships in Academic Societies 【 Display / hide

  • Institute of Electrical and Electronics Engineers, 

    2021.07
    -
    Present
  • The Japan Society of Mechanical Engineers, 

    2021.01
    -
    Present
  • The Mathematical Society of Japan, 

    2018.10
    -
    Present
  • The Japan Society for Industrial and Applied Mathematics, 

    2018.06
    -
    Present