Muramatsu, Mayu

写真a

Affiliation

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

Position

Associate Professor
Scopus Paper Info  
Paper Count: 77 Citation Count: 674 h-index: 14

Click to view the Scopus page. The data was downloaded from Scopus API in September 06, 2026, via http://api.elsevier.com and http://www.scopus.com .

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Career 【 Display / hide 】

  • 2011.04
    -
    Present

    Keio University, Faculty of Science and Technology,　Department of Mechanical Engineering,Research Associate(Non-tenured)

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Academic Background 【 Display / hide 】

  • 2007.03

    Keio University, Faculty of Science, Department of Mechanical Engineering

    University, Graduated

  • 2009.03

    Keio University, Graduate School, Division of Science and Engineering, School of Integrated Design Engineering

    Graduate School, Completed, Master's course

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Academic Degrees 【 Display / hide 】

  • Master of Engineering, Keio University, 2009.03

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Papers 【 Display / hide 】

  • Hamiltonian simulation for nonlinear partial differential equation by Schrödingerization

    Sasaki S., Endo K., Muramatsu M.

    Scientific Reports 16 ( 1 )  2026.12

    　View Summary

    Hamiltonian simulation is a fundamental algorithm in quantum computing that has attracted considerable interest owing to its potential to efficiently solve the governing equations of large-scale classical systems. Exponential speedup through Hamiltonian simulation has been rigorously demonstrated in the case of coupled harmonic oscillators. The question arises as to whether Hamiltonian simulations in other physical systems also accelerate exponentially. Schrödingerization is a technique that transforms the governing equations of classical systems into the Schrödinger equation. However, since the Schrödinger equation is a linear equation, Hamiltonian simulation is often limited to linear equations. The research on Hamiltonian simulation methods for nonlinear governing equations remains relatively limited. In this study, we propose a Hamiltonian simulation method for nonlinear partial differential equations (PDEs). The proposed method is named Carleman linearization + Schrödingerization (CLS), which combines Carleman linearization (CL) and warped phase transformation (WPT). CL is first applied to transform a nonlinear PDE into a linear differential equation. This linearized equation is then mapped to the Schrödinger equation via WPT. The original nonlinear PDE can be solved efficiently by the Hamiltonian simulation of the resulting Schrödinger equation. By applying this method, we transform the original governing equation into the Schrödinger equation. Solving the transformed Schrödinger equation then enables the analysis of the original nonlinear equation. As a specific application, we apply this method to the nonlinear reaction–diffusion equation to demonstrate that Hamiltonian simulations are applicable to nonlinear PDEs.

  • Predicting inclusion-type internal defects in composites with complex structural shapes from experimental infrared thermography and numerical simulations: A demonstration of a machine learning approach based on graph neural networks

    Kojima Y., Yvonnet J., Endo K., Harada Y., Muramatsu M.

    Composites Part B Engineering 326 2026.11

    ISSN  13598368

    　View Summary

    This study proposes a machine learning-based framework for predicting three-dimensional internal defects in a complex-shaped carbon fiber reinforced plastic (CFRP) specimen, using experimentally measured thermoelastic and computational FEA stress distributions. The proposed method employs a graph neural network (GNN), which directly incorporates the FEA mesh. First, high-frequency noise in the thermoelastic two-dimensional stress distribution is removed via Fourier analysis. Then the denoised field is mapped, using partial least squares and Gaussian process regression, into the principal component analysis (PCA) latent space of two-dimensional FEA stress distributions. Next, a multi-layer perceptron projects the latent variables of the two-dimensional stress fields into the latent space of the three-dimensional FEA stress distributions. The resulting latent variables are then reconstructed via PCA to obtain the three-dimensional stress distribution. Finally, the reconstructed three-dimensional stress distribution is input to a GNN trained on FEA datasets to predict three-dimensional internal defects. Conventional nondestructive inspection techniques for CFRP structures, such as X-ray imaging and ultrasonic testing, face safety, cost, and geometric constraints. In contrast, infrared stress measurement offers safer, simpler, and real-time inspection within the field of view. The proposed method estimates internal defect positions from surface stress information and provides a basis for reducing expert-dependent interpretation in future workflows. The present study demonstrates the feasibility of the proposed framework using one complex-shaped CFRP specimen with curved surfaces, such as a prosthetic-leg geometry, under the prescribed defect conditions.

  • Secant-based multiscale constitutive modeling toward coupled homogenized finite element method and molecular dynamics for polycarbonate

    Terashima Y.L., Brumby P.E., Murashima T., Kouznetsova V., Muramatsu M.

    Materials Today Communications 54 2026.06

    　View Summary

    This study presents a secant-based multiscale constitutive modeling framework coupling homogenized finite element method (FEM) and molecular dynamics (MD) for the nonlinear and strain-dependent mechanical behavior of polymeric materials. Representative unit cells governed by MD simulations are assigned to each FEM integration point and deformed consistently with the macroscopic deformation gradient. In conventional atomistic-to-continuum approaches, constitutive responses are often derived from analytically evaluated tangent stiffness at the reference configuration, which is feasible only for simple interatomic potentials. For realistic polymeric systems, however, analytical evaluation of tangent stiffness is generally intractable, and the discrepancy in boundary condition treatment between FEM and MD further complicates direct constitutive coupling. To overcome these difficulties, the proposed framework employs a secant-based approach that enables the extraction of physically meaningful secant properties, such as the secant modulus and transverse contraction coefficient, from temporally averaged MD stress and strain data, which in turn serve as effective constitutive descriptors within the homogenization framework. The proposed method is demonstrated through three numerical examples: uniform tensile simulation on a single-element model, non-uniform tensile simulation on a single-element model, and tensile simulation on a multi-element model of a quarter perforated structure. The results show that the framework successfully reproduces key features of the nonlinear stress–strain behavior obtained from pure MD simulations, while preserving the continuum-scale consistency required for FEM. Overall, the proposed approach provides a robust pathway for incorporating atomistic dynamics into continuum constitutive modeling of polymeric materials.

  • A physics-informed meta-learning framework for the continuous solution of parametric PDEs on arbitrary geometries

    Najian Asl R., Yamazaki Y., Taghikhani K., Muramatsu M., Apel M., Rezaei S.

    Computers and Structures 322 2026.02

    ISSN  00457949

    　View Summary

    In this work, we introduce implicit Finite Operator Learning (iFOL) for the continuous and parametric solution of partial differential equations (PDEs) on arbitrary geometries. We propose a physics-informed encoder-decoder network to establish the mapping between continuous parameter and solution spaces. The decoder constructs the parametric solution field by leveraging an implicit neural field network conditioned on a latent or feature code. Instance-specific codes are derived through a PDE encoding process based on the second-order meta-learning technique. iFOL employs a purely physics-informed loss function derived via the Method of Weighted Residuals. The predicted neural field serves as the test function, resulting in the backpropagation of discrete residuals during the PDE encoding and decoding stages. Compared to the state-of-the-art neural operators, iFOL introduces several key innovations: (1) it bypasses the costly multi-network and supervised encode–process–decode pipeline of conditional neural fields for parametric PDEs; (2) it yields accurate parametric fields and solution-to-parameter gradients, enabling efficient sensitivity analysis regardless of response count; (3) it effectively captures sharp solution discontinuities, which are often challenging for some neural operator models; and (4) it is mesh and geometry agnostic, enabling zero-shot generalization to arbitrary domains. We critically assess these features and analyze the network's ability to generalize to unseen samples across both stationary and transient PDEs. The method is also compared against baseline operator-learning approaches, demonstrating its potential for tackling complex problems in computational mechanics.

  • An Ising machine formulation for design updates in topology optimization of flow channels

    Suzuki Y., Aoki S., Key F., Endo K., Matsuda Y., Tanaka S., Behr M., Muramatsu M.

    Engineering with Computers 42 ( 1 )  2026.02

    ISSN  01770667

    　View Summary

    Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated applicability to other engineering tasks by tailoring corresponding problem formulations. In this study, we present a novel Ising machine formulation for computing design updates during topology optimization with the goal of minimizing dissipation energy in flow channels. We explore the potential of this approach to improve the efficiency and performance of the optimization process. To this end, we conduct experiments to study the impact of various factors within the novel formulation. Additionally, we compare it to a classical method from the literature using the number of optimization steps and the final values of the objective function as indicators of the time intensity of the optimization and the performance of the resulting designs, respectively. Our findings show that the proposed update strategy can accelerate the topology optimization process while producing comparable designs. However, it tends to be less exploratory, which may lead to lower performance of the designs. These results highlight the potential of incorporating Ising formulations for optimization tasks but also show their limitations when used to compute design updates in an iterative optimization process. In conclusion, this work provides an efficient alternative for design updates in topology optimization and enhances the understanding of integrating Ising machine formulations in engineering optimization.

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

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

  • 粘り強い高効率SOFC実現のための強弾性-破壊連成モデルの開発と検証

    2022.04
    -
    2026.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, 基盤研究(B), Principal investigator

  • ミクロ・メゾダイナミクス相互連成による強弾性構成モデルの開発とSOFCへの展開

    2019.04
    -
    2021.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Early-Career Scientists , Principal investigator

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Courses Taught 【 Display / hide 】

  • GRADUATE RESEARCH ON SCIENCE FOR OPEN AND ENVIRONMENTAL SYSTEMS 2

    2026

  • DOCTORAL RESEARCH ON INFORMATICS, MANAGEMENT, AND HUMAN SCIENCES

    2026

  • GRADUATE RESEARCH ON SCIENCE FOR OPEN AND ENVIRONMENTAL SYSTEMS 1

    2026

  • MECHANICAL ENGINEERING PRACTICAL RESEARCH C

    2026

  • GRADUATE RESEARCH ON ENGINEERING AND DESIGN 1

    2026

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