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YAGUCHI TakaharuGraduate School of Science / Division of MathematicsProfessor
Researcher basic information
■ Research news■ Research Keyword
■ Research Areas
■ Committee History
- Apr. 2021 - Present, MDPI Mathematics, Topic Editor
- May 2015 - Mar. 2024, 日本学術会議, 計算音響学小委員会 委員
- Oct. 2019 - Sep. 2021, 日本数学会応用数学分科会委員会委員
- Apr. 2018 - Mar. 2021, 日本応用数理学会, JSIAM Letters 幹事編集委員長
- Sep. 2015 - Mar. 2018, 日本応用数理学会, JSIAM Letters 副編集委員長
- Apr. 2015 - Mar. 2017, 日本応用数理学会, 若手の会 幹事
- 28th International Conference on Artificial Neural Networks, Programme Committee
Research activity information
■ Award- Oct. 2024 神戸大学, 学長表彰（財務貢献者）
- Oct. 2023 神戸大学, 学長表彰（財務貢献者）
- Sep. 2023 JSIAM Letters Paper Award, JSIAM Letters Paper Award, Causal inference for empirical dynamical systems based on persistent homology
- Aug. 2021 日本応用数理学会, 日本応用数理学会論文賞 理論部門, 波動方程式と弾性方程式からなる連成系のシンプレクティック性について
- Sep. 2017 日本応用数理学会, 日本応用数理学会論文賞（理論部門）, ハミルトン方程式に対する離散勾配法のRiemann構造不変性Official journal
- Jun. 2016 日本応用数理学会, 日本応用数理学会研究部会連合発表会優秀講演賞, 第12回日本応用数理学会研究部会連合発表会における講演「自動離散微分とその応用」Japan society
- Sep. 2014 日本応用数理学会, 日本応用数理学会論文賞（理論部門）, コンパクト差分に基づく離散変分導関数法
- Aug. 2012 日本応用数理学会, 日本応用数理学会若手優秀講演賞, ホロノミック系に対するラグランジュ力学的離散勾配法
- Jul. 2011 SciCADE 2011 (the International Conference on Scientific Computation And Differential Equations 2011), SciCADE 2011 New Talent Award, A Lagrangian Approach to Deriving Energy-Preserving Numerical Schemes for the Euler-Lagrange Partial Differential Equations and Its Applications
- May 2025, Proc. of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS2025), EnglishEnergy-Consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations[Refereed]International conference proceedings
- Apr. 2025, Proc. of the Thirteenth International Conference on Learning Representations (ICLR2025), EnglishPoisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains[Refereed]International conference proceedings
- Last, Feb. 2025, Proc. of the 39th Annual AAAI Conference on Artificial Intelligence(AAAI2025), EnglishNumber Theoretic Accelerated Learning of Physics-Informed Neural Networks[Refereed]International conference proceedings
- 2025, IEEE Transactions on Neural Networks and Learning SystemsScientific journal
- null, Dec. 2024, NeurIPS 2024 Workshop on Machine Learning and the Physical Sciences, EnglishPort-Hamiltonian Neural Networks for Learning Coupled Systems and Their Interactions[Refereed]International conference proceedings
- null, Dec. 2024, Proc. of 2024 International Symposium on Nonlinear Theory and Its Applications (NOLTA2024), EnglishLearning Difference and Summation Operators for Discretization of Nonlocal Hamiltonian Partial Differential Equations Using Neural Networks[Refereed]International conference proceedings
- null, Dec. 2024, Proc. of 2024 International Symposium on Nonlinear Theory and Its Applications (NOLTA2024), EnglishApplication of the Kernel Method to Learning Symplectic Forms[Refereed]International conference proceedings
- null, Dec. 2024, Proc. of 2024 International Symposium on Nonlinear Theory and Its Applications (NOLTA2024), EnglishA New Approach to Designing Robust Hamiltonian Neural Networks by Regularisation[Refereed]International conference proceedings
- null, Dec. 2024, Proc. of 2024 International Symposium on Nonlinear Theory and Its Applications (NOLTA2024), EnglishHyperbolic-PDE-Based Neural Network Architecture[Refereed]International conference proceedings
- Elsevier BV, Oct. 2024, Physica D: Nonlinear Phenomena, 134382 - 134382, English[Refereed]Scientific journal
- null, Jul. 2024, IEICE Transactions on Information and Systems, E107-D, EnglishLoss Function for Deep Learning to Model Dynamical Systems[Refereed]Scientific journal
- null, Jun. 2024, Proc. of CAI2024 Workshop on Scientific Machine Learning and Its Industrial Applications (SMLIA2024), EnglishImproved input points estimate for identifying nonlinear dynamic systems in DeepONet[Refereed]International conference proceedings
- null, Jun. 2024, Proc. of CAI2024 Workshop on Scientific Machine Learning and Its Industrial Applications (SMLIA2024), EnglishLearning Coupled Systems and their Connectivity Using Port-Hamiltonian Neural Networks[Refereed]International conference proceedings
- Apr. 2024, Mathematics, English[Refereed]Scientific journal
- Estimating governing equations from observed time-series data is crucial for understanding dynamical systems. From the perspective of system comprehension, the demand for accurate estimation and interpretable results has been particularly emphasized. Herein, we propose a novel data-driven method for estimating the governing equations of dynamical systems based on machine learning with high accuracy and interpretability. The proposed method enhances the estimation accuracy for dynamical systems using sparse modeling by incorporating physical constraints derived from Hamiltonian mechanics. Unlike conventional approaches used for estimating governing equations for dynamical systems, we employ a sparse representation of Hamiltonian, allowing for the estimation. Using noisy observational data, the proposed method demonstrates a capability to achieve accurate parameter estimation and extraction of essential nonlinear terms. In addition, it is shown that estimations based on energy conservation principles exhibit superior accuracy in long-term predictions. These results collectively indicate that the proposed method accurately estimates dynamical systems while maintaining interpretability.MDPI AG, Mar. 2024, Mathematics, 12(7) (7), 974 - 974, English[Refereed]Scientific journal
- Dec. 2023, NeurIPS2023 Workshop: Machine Learning with New Compute Paradigms, English, International magazineAlgebraic Design of Physical Computing System for Time-Series Generation[Refereed]International conference proceedings
- Sep. 2023, IEICE Proceedings Series, 76, 419 - 421, English, International magazineApplication of the Neural Operator for Physical Simulations of GENERIC Systems[Refereed]International conference proceedings
- Sep. 2023, IEICE Proceedings Series, 76, 370 - 373, English, International magazineSuper Resolution of Numerical Solutions of Nonlinear Elliptic Equations by DeepONet[Refereed]International conference proceedings
- Sep. 2023, IEICE Proceedings Series, 76, 259 - 262, English, International magazineGeneralization Error Analysis of Discrete Hamiltonian Neural Networks[Refereed]International conference proceedings
- Jul. 2023, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, English, International magazineVariational Principle and Variational Integrators for Neural Symplectic Forms[Refereed]International conference proceedings
- Jul. 2023, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, English, International magazineEquivalence Class Learning for GENERIC Systems[Refereed]International conference proceedings
- Jul. 2023, 1st Workshop on the Synergy of Scientific and Machine Learning Modeling at ICML2023, English, International magazineGood Lattice Accelerates Physics-Informed Neural Networks[Refereed]International conference proceedings
- May 2023, Proc. of The Eleventh International Conference on Learning Representations (ICLR2023), 11, English, International magazineFINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities[Refereed]International conference proceedings
- Institute of Electrical and Electronics Engineers (IEEE), 2023, IEEE Transactions on Neural Networks and Learning Systems, 1 - 13Scientific journal
- Oct. 2022, 応用物理, 91(10) (10), 629 - 633, Japanese幾何学的深層科学技術計算 -深層学習による物理モデリング・シミュレーション-[Invited]Scientific journal
- The Japan Society for Industrial and Applied Mathematics, Mar. 2022, JSIAM Letters, 14, 37 - 40, English[Refereed]Scientific journal
- Last, Feb. 2022, Thirty-Sixth AAAI Conference on Artificial Intelligence, EnglishKAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss[Refereed]International conference proceedings
- 2022, Proceedings of the 2022 International Symposium on Nonlinear Theory and its Applications (NOLTA2022)Learning GENERIC Systems Using Neural Symplectic Forms[Refereed]
- 2022, Proceedings of the 2022 International Symposium on Nonlinear Theory and its Applications (NOLTA2022), EnglishVariational Integrator for Hamiltonian Neural Networks[Refereed]International conference proceedings
- 2022, Journal of Signal Processing, JapaneseSecure Communication Systems Based on Synchronization of Chaotic Vibration of Wave Equations[Refereed]Scientific journal
- 2022, ICLR2022 Workshop on AI for Earth and Space Science (ai4earth), EnglishImbalance-Aware Learning for Deep Physics Modeling[Refereed]International conference proceedings
- Dec. 2021, Advances in Neural Information Processing Systems (NeurIPS), 34, EnglishNeural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems[Refereed]International conference proceedings
- Dec. 2021, Advances in Neural Information Processing Systems (NeurIPS), 34, EnglishSymplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory[Refereed]International conference proceedings
- In a secret communication system using chaotic synchronization, the communication information is embedded in a signal that behaves as chaos and is sent to the receiver to retrieve the information. In a previous study, a chaotic synchronous system was developed by integrating the wave equation with the van der Pol boundary condition, of which the number of the parameters are only three, which is not enough for security. In this study, we replace the nonlinear boundary condition with an artificial neural network, thereby making the transmitted information difficult to leak. The neural network is divided into two parts; the first half is used as the left boundary condition of the wave equation and the second half is used as that on the right boundary, thus replacing the original nonlinear boundary condition. We also show the results for both monochrome and color images and evaluate the security performance. In particular, it is shown that the encrypted images are almost identical regardless of the input images. The learning performance of the neural network is also investigated. The calculated Lyapunov exponent shows that the learned neural network causes some chaotic vibration effect. The information in the original image is completely invisible when viewed through the image obtained after being concealed by the proposed system. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method.MDPI AG, Jul. 2021, Entropy, 23(7) (7), 904 - 904, English[Refereed]Scientific journal
- Institute of Electronics, Information and Communications Engineers (IEICE), Jul. 2021, Nonlinear Theory and Its Applications, IEICE, 12(3) (3), 295 - 308, English[Refereed][Invited]Scientific journal
- May 2021, ICLR2021 Workshop on Deep Learning for Simulation (SimDL),, EnglishDeep Discrete- Time Lagrangian Mechanics[Refereed]International conference proceedings
- Feb. 2021, Transactions of the Society of Instrument and Control Engineers, 57(2) (2), 78 - 85, JapaneseSecure Communication Systems Using Distributed Parameter Chaotic Synchronization[Refereed]Scientific journal
- IEEE, Jan. 2021, Proceedings of 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), 1 - 4, English[Refereed]International conference proceedings
- Dec. 2020, Transactions of the Japan Society for Industrial and Applied Mathematics, 30(4) (4), 269 - 289, JapaneseSimplecticity of Coupled System of the Wave Equation and the Elastic Equation[Refereed]Scientific journal
- Dec. 2020, Advances in Neural Information Processing Systems (NeurIPS), 33, 13100 - 13111, EnglishDeep Energy-Based Modeling of Discrete-Time Physics[Refereed]International conference proceedings
- Nov. 2020, Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications (NOLTA2020), 204 - 207, EnglishParameter estimation for dynamical systems via structural realization[Refereed]International conference proceedings
- Recently, soft robots that consist of soft and deformable materials have received much attention for their adaptability to uncertain environments. Although these robots are difficult to control with a conventional control theory owing to their complex body dynamics, research from different perspectives attempts to actively exploit these body dynamics as an asset rather than a drawback. This approach is called morphological computation, in which the soft materials are used for computation that includes a new kind of control strategy. In this article, we propose a novel approach to analyze the computational properties of soft materials based on an algebraic method, called the input–output equation used in systems analysis, particularly in systems biology. We mainly focus on the two scenarios relevant to soft robotics, that is, analysis of the computational capabilities of soft materials and design of the input force to soft devices to generate the target behaviors. The input–output equation directly describes the relationship between inputs and outputs of a system, and hence by using this equation, important properties, such as the echo state property that guarantees reproducible responses against the same input stream, can be investigated for soft structures. Several application scenarios of our proposed method are demonstrated using typical soft robotic settings in detail, including linear/nonlinear models and hydrogels driven by chemical reactions.SAGE Publications, Mar. 2020, The International Journal of Robotics Research, 40(1) (1), 027836492091229 - 027836492091229, English[Refereed]Scientific journal
- {MDPI} {AG}, Feb. 2020, Mathematics, 8(2) (2), 249 - 249, English[Refereed][Invited]Scientific journal
- Dec. 2019, Proceedings of the 2019 International Symposium on Nonlinear Theory and its Applications (NOLTA2019), 187 - 190, EnglishDifferential Algebraic Method for Direct Evaluation of Computational Capabilities of Physical Reservoirs[Refereed]International conference proceedings
- Jan. 2019, Japan Journal of Industrial and Applied Mathematics, 36(1) (1), 3 - -24, English[Refereed]Scientific journal
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11141 LNCS, 781 - 794, English[Refereed]Scientific journal
- 2018, 日本応用数理学会論文誌, 28, 162 - 204, JapaneseHusbyらの実験データに対するアレルギー発症メカニズムの解析に向けた抗原・抗体の体内動態モデルの構築[Refereed]Scientific journal
- Jan. 2018, Japan Journal of Industrial and Applied Mathematics, 35(2) (2), English[Refereed]Scientific journal
- Oct. 2017, GERIATRICS & GERONTOLOGY INTERNATIONAL, 17(10) (10), 1752 - 1758, English[Refereed]Scientific journal
- Mar. 2017, 応用数理, 27, 13 - 20, Japanese[Refereed][Invited]Scientific journal
- 日本応用数理学会 ; 1991-, Mar. 2017, 応用数理, 27(1) (1), 13 - 20, Japanese[Refereed][Invited]Scientific journal
- Mar. 2017, MI Lecture Notes of IMI, 74, 31 - 33, EnglishGeometric-mechanics-inspired model of stochastic dynamical systemsInternational conference proceedings
- Mar. 2017, MI Lecture Notes of IMI, 74, 63 - 68, EnglishEnergy-preserving Discrete Gradient Schemes for the Hamilton Equation Based on the Variational PrincipleInternational conference proceedings
- Dec. 2016, 日本応用数理学会論文誌, 26(4) (4), 381 - 415, Japanese[Refereed]Scientific journal
- 神戸大学経済経営学会, Nov. 2016, 國民經濟雜誌, 214(5) (5), 39 - 50, JapaneseScientific journal
- The Japan Society for Industrial and Applied Mathematics, Sep. 2016, JSIAM Letters, 8, 53 - 56, English
In this contribution, we propose a new framework to derive energy-preserving numerical schemes based on the variational principle for Hamiltonian mechanics. We focus on Noether's theorem, which shows that the symmetry with respect to time translation gives the energy conservation law. By reproducing the calculation of the proof of Noether's theorem after discretization using the summation by parts and the discrete gradient, we obtain the scheme and the corresponding discrete energy at the same time. The significant property of efficiency is that the appropriate choice of the discrete gradient makes our schemes explicit if the Hamiltonian is separable.
[Refereed]Scientific journal - Dec. 2015, 2015年度応用数学合同研究集会予稿集, 394 - 401, Japanese地域コミュニティの構造変化に対する検定理論Symposium
- 2015, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014), 1648, English[Refereed]International conference proceedings
- 2015, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014), 1648, English[Refereed]International conference proceedings
- We consider application of the discrete gradient method for the Webster equation, which models sound waves in tubes. Typically Hamilton equations are described by the use of gradients of the Hamiltonian and it is indispensable to introduce an inner product to define a gradient. We first apply the discrete gradient method to design an energy-preserving method by using a weighted inner product. Comparing with another scheme that is derived by a standard inner product, we show that the discrete gradient method has a geometric invariance, which implies that the method reflects the symplectic geometric aspect of mechanics.The Japan Society for Industrial and Applied Mathematics, Jan. 2015, JSIAM Letters, 7, 17 - 20, English[Refereed]Scientific journal
- Sep. 2013, ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS-MODELISATION MATHEMATIQUE ET ANALYSE NUMERIQUE, 47(5) (5), 1493 - 1513, English[Refereed]Scientific journal
- For partial differential equations having conserved quantities, such as soliton equations, the "structure-preserving methods" which preserve the invariants are advantageous. On the other hand, in the field of computational fluid dynamics, a special difference method, called "compact difference method," has been widely used due to its high efficiency in wave propagation problems. In this paper, it is shown that the two methods can be combined, i.e., the compact difference method can be incorporated into a structure-preserving method, "the discrete variational derivative method," to construct efficient conservative finite difference schemes. Several numerical experiments are also included.The Japan Society for Industrial and Applied Mathematics, Jun. 2013, 日本応用数理学会論文誌, 23(2) (2), 203 - 232, Japanese[Refereed]Scientific journal
- We propose a Lagrangian approach to deriving local-energy-preserving finite difference schemes for the Euler-Lagrange partial differential equations regarding that, from Noether's theorem, the symmetry of time translation of Lagrangian yields the energy conservation law. We first observe that the local symmetry of time translation of Lagrangian derives the Euler-Lagrange equation and the energy conservation law, simultaneously. The new method is a combination of a discrete counter part of this statement and the discrete gradient method. As an application of the discrete local energy conservation law, we also discuss discretization of the nonreflecting boundary conditions for the linear wave equation.The Japan Society for Industrial and Applied Mathematics, Sep. 2012, 日本応用数理学会論文誌, 22(3) (3), 143 - 169, Japanese[Refereed]Scientific journal
- May 2012, JOURNAL OF COMPUTATIONAL PHYSICS, 231(10) (10), 3963 - 3986, English[Refereed]Scientific journal
- May 2012, JOURNAL OF COMPUTATIONAL PHYSICS, 231(14) (14), 4542 - 4559, English[Refereed]Scientific journal
- We propose a new structure-preserving integrator for the Korteweg-de Vries (KdV) equation. In this integrator, two independent structure-preserving techniques are newly combined; the "discrete variational derivative method" for constructing invariants-preserving integrator, and the "compact finite difference method" which is widely used in the area of numerical fluid dynamics for resolving wave propagation phenomena. Numerical experiments show that the new integrator is in fact advantageous than the existing integrators.The Japan Society for Industrial and Applied Mathematics, Mar. 2012, JSIAM Letters, vol. 4, 5-8., 5 - 8, English[Refereed]Scientific journal
- Jul. 2011, JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 44(30) (30), English[Refereed]Scientific journal
- We consider structure-preserving integration of the Ostrovsky equation, which for example models gravity waves under the influence of Coriolis force. We find a multi-symplectic formulation, and derive a finite difference discretization based on the formulation and by means of the Preissman box scheme. We also present a numerical example, which shows the effectiveness of this scheme.The Japan Society for Industrial and Applied Mathematics, Jun. 2011, JSIAM Letters, vol. 3, 41-44., 41 - 44, English[Refereed]Scientific journal
- Dec. 2010, JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS, 27(3) (3), 425 - 441, English[Refereed]Scientific journal
- Jun. 2010, JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 234(4) (4), 1036 - 1048, English[Refereed]Scientific journal
- Jun. 2010, JOURNAL OF COMPUTATIONAL PHYSICS, 229(11) (11), 4382 - 4423, English[Refereed]Scientific journal
- The Japan Society for Industrial and Applied Mathematics, 2010, Bulletin of the Japan Society for Industrial and Applied Mathematics, 20(1) (1), 75 - 76, Japanese
- The Japan Society for Industrial and Applied Mathematics, 2009, Bulletin of the Japan Society for Industrial and Applied Mathematics, 19(3) (3), 205 - 206, Japanese
- 2009, NUMERICAL ANALYSIS AND APPLIED MATHEMATICS, VOLS 1 AND 2, 1168, 892 - 895, EnglishAn Energy Conservative Numerical Scheme on Mixed Meshes for the Nonlinear Schrodinger Equation[Refereed]International conference proceedings
- The discrete variational method is a method to derive finite difference schemes that inherit the conservation/dissipation properties of the original equations. Although this method has been developed on uniform grids, we show that this method is also applicable to multi-dimensional non-uniform grids.The Japan Society for Industrial and Applied Mathematics, 2009, Transactions of the Japan Society for Industrial and Applied Mathematics, 19(4) (4), 371-431 - 431, Japanese[Refereed]Scientific journal
- Since the computational resources are finite, one must truncate the computational domain into finite when he/she simulates waves in an unbounded region. However this truncation gives rise to artificial boundaries and boundary conditions on such the artificial boundaries greatly affect the quality of the numerical solutions. In this paper restricting ourselves to compressible inviscid isentropic flows we derive an artificial boundary condition and an energy-type estimate under that condition. Futhermore the obtained boundary condition is shown to be equivalent to the well-known Thompson boundary condition.The Japan Society for Industrial and Applied Mathematics, 2008, Transactions of the Japan Society for Industrial and Applied Mathematics, 18(3) (3), 447 - 471, Japanese
- 2006, Journal of Computational and Applied Mathematics, 197(1) (1)Scientific journal
- Jun. 2016, シミュレーション, 35(2) (2), Japanese微分方程式モデルによる楽器シミュレーション[Invited]Introduction scientific journal
- 京都大学, Jul. 2015, 数理解析研究所講究録, 1957, 14 - 26, JapaneseWebster方程式に対する離散勾配法とその力学的不変性について (新時代の科学技術を牽引する数値解析学)
- 日本数学会, 2014, 数学, 66(1) (1), 107 - 111, Japanese書評 D. Furihata and T. Matsuo : Discrete Variational Derivative Method : A Structure-Preserving Numerical Method for Partial Differential Equations
- 京都大学, Apr. 2012, 数理解析研究所講究録, 1791, 87 - 96, Japaneseある半離散スキームによるソリトンシミュレーションについて (科学技術計算における理論と応用の新展開)
- 京都大学, Nov. 2010, 数理解析研究所講究録, 1719, 61 - 73, Japaneseハミルトン偏微分方程式に対する解析力学的空間離散化法とその応用 (数値解析と数値計算アルゴリズムの最近の展開)
- The Design of Nonreflecting Boundaries for Numerical Simulations of Waves(
Superrobust Computation and Modeling/Simulation) Nonreflecting boundary conditions for numerical simulations of waves are reviewed. We describe the idea of the classical Engquist-Majda boundary condition for linear wave equations and the Hedstrom boundary condition for quasilinear hyperbolic systems. Some comments on the theoretical aspects of the boundary treatments such as the validity of the nonreflecting boundary conditions are provided. The recent developments on this subject are also discussed.Japan Society for Simmulation Technology, 15 Jun. 2007, Journal of the Japan Society for Simulation Technology, 26(2) (2), 84 - 89, Japanese - A Characteristic Nonreflecting Boundary Condition for the Multidimensional Navier-Stokes EquationsBecause the computational resources are finite, one needs to truncate the computational domain when he/she simulates a physical problem. This truncation gives rise to non-physical artificial boundaries and one cannot obtain proper solutions unless appropriate boundary conditions on such boundaries are imposed. Practically nonreflecting boundary conditions, which are boundary conditions that prevent the generation of reflections, are of great importance. By the reason of the practical robustness and the simplicity of implementation, the Poinsot-Lele boundary condition is one of the most popular methods for the Navier-Stokes equations right now. Their method is based on Thompson's boundary condition for the Euler equations, which, however, is essentially one-dimensional. Therefore the Poinsot-Lele boundary condition is valid only when the flow is perpendicular to the boundary theoretically. Here we propose a nonreflecting boundary condition for the Euler equations which does not have the assumption on the direction of flow. We also discuss its extension to the Navier-Stokes equations. Our basic idea is to estimate the direction of the flow from numerical data.Japan Society of Fluid Mechanics, 25 Feb. 2005, Journal of Japan Society of Fluid Mechanics, 24(1) (1), 81 - 91, Japanese
- G221 New Nonreflecting Boundary Condition Based on Method of CharacteristicsBecause the computational resources are finite, one needs to truncate the computational domain when he/she simulates a physical problem. This truncation gives rise to non-physical artificial boundaries and one cannot obtain proper solutions without appropriate boundary conditions on such boundaries. Practically nonreflecting boundary conditions, which are boundary conditions that prevent the generation of reflections, are of great importance. Most popular methods for the Navier-Stokes equations right now are boundary conditions by Poinsot and Lele. However, their methods are based on Thompson's boundary condition for the Euler equations, which are essentially one-dimensional, and hence are valid only when the flow is perpendicular to the boundary. Here we propose a boundary condition for the Navier-Stokes equations which does not require the assumption for the direction of flow. Our basic idea is to estimate the direction of the flow with numerical data.日本流体力学会, 2004, 日本流体力学会年会講演論文集, 2004, 456 - 457, Japanese
- 日本数学会2025年度年会, Mar. 2025, Japanese, Domestic conferenceNavier–Stokes 方程式に対する PINNs の解の誤差解析Oral presentation
- 日本数学会2025年度年会, Mar. 2025, Japanese, Domestic conference幾何学的深層科学技術計算[Invited]Invited oral presentation
- International Conference on Scientific Computing and Machine Learning 2025, Mar. 2025, Japanese, Domestic conferenceModeling Coupled Systems by Neural Networks with Poisson Structures and PortsOral presentation
- International Conference on Scientific Computing and Machine Learning 2025, Mar. 2025, Japanese, Domestic conferenceRefinement of the average vector field method for Hamiltonian systems using neural networksOral presentation
- International Conference on Scientific Computing and Machine Learning 2025, Mar. 2025, Japanese, Domestic conferenceLearning Hamiltonian Partial Differential Equations Using DeepONet with a Symplectic Branch NetworkOral presentation
- International Conference on Scientific Computing and Machine Learning 2025, Mar. 2025, Japanese, Domestic conferenceLearning Hamiltonian Density Using DeepONet for Modeling Wave EquationsOral presentation
- International Conference on Scientific Computing and Machine Learning 2025, Mar. 2025, Japanese, Domestic conferenceAn Infinite Dimensional LSSL with Infinite Dimensional HiPPOOral presentation
- International Conference on Scientific Computing and Machine Learning 2025, Mar. 2025, Japanese, Domestic conferenceEnergy-consistent Neural Operator LearningOral presentation
- Workshop on Dynamical Systems and Machine Learning, Feb. 2025, English, International conferenceModel Reduction of Neural Operators by Infinite-Dimensional Singular Value Decomposition[Invited]Invited oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第28回シンポジウム, Dec. 2024, Japanese, Domestic conferenceGe-Marsden の定理に基づくSympNets の改良の試みOral presentation
- Geometric Structures and Differential Equations -- Symmetry, Singularity, and Dynamical Systems --, Dec. 2024, English, International conferenceOn a posteriori estimates of physics-informed neural networks for solving partial differential equations[Invited]Invited oral presentation
- 第27回情報論的学習理論ワークショップ (IBIS2024), Nov. 2024, Japanese, Domestic conference波動方程式のハミルトニアン密度のDeepONetによる作用素学習Oral presentation
- 第49回ASE研究会開催, Oct. 2024, Japanese, Domestic conference深層科学技術計算[Invited]Invited oral presentation
- Plasma Simulator Symposium 2024, Sep. 2024, Japanese, Domestic conference深層科学技術計算：深層学習の物理モデリング・シミュレーションへの応用[Invited]Invited oral presentation
- 日本応用数理学会2024年度年会, Sep. 2024, Japanese, Domestic conference非線形波動のモデリングのためのハミルトニアン密度の作用素学習Oral presentation
- REMODEL-DSC Workshop on Machine Learning and Physics, Aug. 2024, English, International conferenceHyperbolic Partial Differential Equations Derived From Hippo MatricesPoster presentation
- REMODEL-DSC Workshop on Machine Learning and Physics, Aug. 2024, English, International conferenceApplication of DeepONet for learning Hamiltonian PDEsPoster presentation
- REMODEL-DSC Workshop on Machine Learning and Physics, Aug. 2024, English, International conferenceStructure-preserving methods for a class of dissipative differential equations[Invited]Invited oral presentation
- REMODEL-DSC Workshop on Structure-Preserving Numerical Methods and Machine Learning, Aug. 2024, English, International conferenceGeometric Deep Energy-Based Models for Physics[Invited]Invited oral presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2024, Jul. 2024, English, International conferenceNeural Operators for Hamiltonian and Dissipative PDEsPoster presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2024, Jul. 2024, English, International conferenceImproved estimate of the number of input points of DeepONetOral presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2024, Jul. 2024, English, International conferenceOperator Learning of Hamiltonian Density for Modeling Nonlinear WavesOral presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2024, Jul. 2024, English, International conferenceEnhancing Modeling Accuracy via Discriminating Hamiltonian SystemsOral presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2024, Jul. 2024, English, International conferenceAn error bound of PINNs for solving differential equationsOral presentation
- 第29回計算工学講演会, Jun. 2024, Japanese, Domestic conferencePINNによってエネルギー保存則・エントロピー増大則を保つGENERIC系の作用素学習Oral presentation
- 第29回計算工学講演会, Jun. 2024, Japanese, Domestic conferencePhysics-Informed Neural Networksの誤差解析についてOral presentation
- The Data Science seminar in University of Birmingham, May 2024, English, International conferenceDeep Learning Models for Physical Modeling[Invited]Invited oral presentation
- PhysML Workshop 2024, May 2024, English, International conferenceAn error bound of physics-informed neural networks for solving differential equations[Invited]Invited oral presentation
- The DNA (Differential Equations and Numerical Analysis) Seminar, May 2024, English, International conferenceDeep Discrete-Time Models for Physics[Invited]Invited oral presentation
- Cambridge Image Analysis Sminar, May 2024, English, International conferenceGeometric Deep Energy-Based Models for Physics[Invited]Invited oral presentation
- MfIP連携探索ワークショップ「数学を軸とする新たな価値創造に向けて」, Apr. 2024, Japanese, Domestic conference深層科学技術計算とそれを支える数学[Invited]Invited oral presentation
- BIRS Workshop: Structured Machine Learning and Time–Stepping for Dynamical Systems, Feb. 2024, English, International conferenceNumerical integrators for learning neural ordinary differential equation models[Invited]Invited oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第27回シンポジウム, Dec. 2023, Japanese, Domestic conferenceDeepONet による発展型偏微分方程式の学習Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第27回シンポジウム, Dec. 2023, Japanese, Domestic conferenceDeepONet による非線形力学系の解の予測における入力点数の評価の改良Oral presentation
- IMI研究集会「新時代における高性能科学技術計算法の探究」, Nov. 2023, Japanese, Domestic conference深層物理モデルにおける数値解析技術の応用について[Invited]Invited oral presentation
- RIMS研究集会「新時代における高性能科学技術計算法の探究」, Oct. 2023, Japanese, Domestic conference幾何学的深層学習[Invited]Invited oral presentation
- 日本数学会2023年度秋季総合分科会, Sep. 2023, Japanese, Domestic conference末梢血造血幹細胞動員データ解析のためのグレブナー基底による変数分 類手法Oral presentation
- 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, Sep. 2023, Japanese, Domestic conferenceアクティブエイジングプロジェクトにおける社会ネットワーク解析[Invited]Invited oral presentation
- 10th International Congress on Industrial and Applied Mathematics (ICIAM2023), Aug. 2023, English, International conferenceApplication of the Kernel Method to Learning Hamiltonian EquationsOral presentation
- 10th International Congress on Industrial and Applied Mathematics (ICIAM2023), Aug. 2023, English, International conferenceStructure-Preserving Learning for GENERIC systemsOral presentation
- 10th International Congress on Industrial and Applied Mathematics (ICIAM2023), Aug. 2023, English, International conferenceGeometric Integrators for Neural Symplectic FormsOral presentation
- Maths4DL Deep Learning for Computational Physics conference, Jul. 2023, English, International conferenceNeural symplectic form and its variational principlePoster presentation
- 2023年度 第37回 人工知能学会全国大会 (JSAI2023), Jun. 2023, Japanese, Domestic conference物理システムにおける深層学習のための損失関数Oral presentation
- 第28回計算工学講演会, Jun. 2023, Japanese, Domestic conferenceカーネル法によるハミルトン系の学習と乱択化による高速化Oral presentation
- 数学と諸分野の連携にむけた若手数学者交流会2023, Mar. 2023, Japanese幾何学的深層科学技術計算 ～深層学習による物理モデリング・ シミュレーション～[Invited]Invited oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第26回シンポジウム, Dec. 2022, Japanese在変数をもつハミルトニアンニューラルネットワークのハミルトン構造をもたないデータへの適用についてOral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第26回シンポジウム, Dec. 2022, Japaneseハミルトン系に対するカーネル法によるモデリングOral presentation
- 第25回情報論的学習理論ワークショップ (IBIS2022), Nov. 2022, Japanese幾何学的力学と深層学習の連携による物理現象の構造保存型モデリング[Invited]Invited oral presentation
- 第35回計算力学講演会, Nov. 2022, Japanese深層科学技術計算の最新動向 ー幾何学的深層科学技術計算ー[Invited]Invited oral presentation
- 日本数学会2022年度秋季総合分科会, Sep. 2022, Japanese神経ネットワーク動画像からのモデリングの試みOral presentation
- 日本数学会2022年度秋季総合分科会, Sep. 2022, Japanese一般化 Dissipative SymODEN の GENERIC 形式Oral presentation
- 日本数学会2022年度秋季総合分科会, Sep. 2022, Japaneseニューラルシンプレクティック形式と変分原理の両立性についてOral presentation
- 日本応用数理学会2022年度年会, Sep. 2022, Japanese複数の研究分野の連携と数理科学[Invited]Invited oral presentation
- 日本応用数理学会2022年度年会, Sep. 2022, JapaneseGENERICシステムに対する構造保存型深層物理モデルOral presentation
- 日本応用数理学会2022年度年会, Sep. 2022, Japanese深層学習を用いてデータから力学系の第一積分を発見し保存するモデル化法Oral presentation
- 日本応用数理学会2022年度年会, Sep. 2022, Japanese交流アンケートデータからのネットワーク特徴量推定についてOral presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2022, Jul. 2022, EnglishLearning GENERIC Systems Using Neural Symplectic FormsOral presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2022, Jul. 2022, EnglishTheoretical analysis of approximation properties of Hamiltonian neural networksOral presentation
- International Conference on Scientific Computation and Differential Equations (SciCADE) 2022, Jul. 2022, EnglishNeural symplectic form and coordinate-free learning of Hamiltonian dynamicsOral presentation
- 電子情報通信学会 情報論的学習理論と機械学習研究会(IBISML), Jun. 2022, Japanese射影法を用いて系の第一積分を発見し保存するNeural ODEOral presentation
- 2022年度 第36回人工知能学会全国大会(JSAI2022), Jun. 2022, Japaneseアンバランスを考慮した深層学習による物理系の学習Oral presentation
- 電子情報通信学会 NOLTAソサイエティ大会, Jun. 2022, JapaneseImbalance-aware lossを用いた深層学習による物理系の学習Oral presentation
- 第27回計算工学講演会, Jun. 2022, JapaneseNeural Symplectic 形式によるGENERICシステムの学習Oral presentation
- Geometric Deep Energy- Based Models for Physics, Workshop on Functional Inference and Machine Intelligence (FIMI2022), 2022, Mar. 2022, EnglishGeometric Deep Energy- Based Models for Physics[Invited]Invited oral presentation
- 電子情報通信学会技術研究報告 複雑コミュニケーションサイエンス研究会(CCS), Mar. 2022, JapaneseLearning Physical Systems with Imbalance-Aware Deep LearningOral presentation
- 日本応用数理学会第18 回研究 部会連合発表会, Mar. 2022, Japanese社会的つながりの次 数分布からの交流ネットワーク生成モデルの提案Oral presentation
- 日本応用数理学会第18 回研究部会連合発表会, Mar. 2022, Japaneseニューラルシンプレクティック形式とその応用Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第25 回シンポジウ ム, Dec. 2021, Japanese非平衡熱力学による摩擦付き質点バネ系に対する数値解法とその刻み幅条件Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第25 回シンポジウ ム, Dec. 2021, Japaneseシンプレクティック形式の学習による一般座標系での 深層物理モデルOral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第25 回シンポジウム, Dec. 2021, Japaneseハミルトニアンニューラルネットワークの安定性についてOral presentation
- 電子情報通信学会技術研究報告複雑コミュニケーションサイ エンス研究会(CCS), Nov. 2021, Japaneseシンプレクティック随伴変数法に基づく省メモリな Neural ODE の学習Oral presentation
- 第24 回情報論的学習理論ワークショップ(IBIS2021), Nov. 2021, Japaneseハミルトニアンニューラルネットワークの理論評価と KAM 理論への応用Oral presentation
- 第24 回情報論的学習理論ワークショップ(IBIS2021), Nov. 2021, Japaneseシンプレクティック随伴変数法による高速省メモリ なNeural ODE の勾配計算Oral presentation
- 第24 回情報論的学習理論ワークショップ (IBIS2021), Nov. 2021, Japaneseニューラルシンプレクティック形式とそれによる一般座標系でのハミルトン方程式の学習Oral presentation
- DMV-OMG Annual Conference 2021, Sep. 2021, EnglishGeometric Energy-Based Deep-Learning Models for Physics[Invited]Invited oral presentation
- 第 31 回日本数理生物学会大会（2021 年度年会）, Sep. 2021, Japanese同定不可能モデルの解析：パラメータ多様体とその展開Oral presentation
- 電子情報通信学会情報論的学習理論と機械学習研究会(IBISML), Jun. 2021, Japaneseシンプレクティック数値積分法を用いたNeural ODE の学習Oral presentation
- 第35 回人工知能学会全国大会(JSAI2021), Jun. 2021, Japanese離散時間ラグランジュ力学のニューラルネットワー クによるモデル化Oral presentation
- 2021 年度第35 回人工知能学会全国大会 (JSAI2021), Jun. 2021, Japanese物理現象のエネルギー挙動を離散時間で保証する深層学習シミュレーションOral presentation
- 第26回計算工学講演会, May 2021, Japanese⼀般座標系におけるエネルギーベース物理モデルOral presentation
- 明治大学共同利用・共同研究拠点研究集会「高度な自動運転を実現するための数理の現状と課題」, Mar. 2021, Japanese, オンライン, Domestic conference深層学習を用いたエネルギーベースのモデリング・シ ミュレーションフレームワーク[Invited]Oral presentation
- 日本応用数理学会第17回研究部会連合発表会, Mar. 2021, Japanese, オンライン, Domestic conferenceKoopman 作用素を利用した発展型ネットワーク予測の試みOral presentation
- 日本応用数理学会第17回研究部会連合発表会, Mar. 2021, Japanese, オンライン, Domestic conferenceアトラクターのトポロジーに着目した因果推定手法についてOral presentation
- 日本応用数理学会第17回研究部会連合発表会, Mar. 2021, Japanese, オンライン, Domestic conference非線形状態空間システム解析における代数的マトロイドの応用についてOral presentation
- Workshop: シミュレーションとモデリングのための計算代数 2021, Feb. 2021, Japanese, オンライン, Domestic conference深層学習によるエネルギーベース物理モデル, その2[Invited]Oral presentation
- Workshop: シミュレーションとモデリングのための計算代数 2021, Feb. 2021, Japanese, オンライン, Domestic conference深層学習によるエネルギーベース物理モデル, その1[Invited]Oral presentation
- 日本ディープラーニング協会主催 NeurIPS 2020 技術報告会, Jan. 2021, Japanese, オンライン, Domestic conferenceDeep Energy-Based Modeling of Discrete-Time Physics[Invited]Oral presentation
- 数値解析セミナー, Jan. 2021, Japanese, オンライン, Domestic conferenceDGNet: エネルギー保存・散逸則を保つ深層物理モデリングとそれに関する理論・応用[Invited]Oral presentation
- 2020年度応用数学合同研究集会, Dec. 2020, Japanese, オンライン, Domestic conference潜在変数をもつニューラル微分方程式に対する代数的考察Oral presentation
- 2020年度応用数学合同研究集会, Dec. 2020, Japanese, オンライン, Domestic conference深層フェーズフィールドモデリングOral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第24回シンポジウム, Dec. 2020, Japanese, オンライン, Domestic conference自然系の連成とシンプレクティック形式Oral presentation
- NeurIPS2020 Workshop on Machine Learning and the Physical Sciences (ML4PS), Dec. 2020, English, オンライン, Domestic conferenceThe Error Analysis of Numerical Integrators for Deep Neural Network Modeling of Differential EquationsPoster presentation
- Establishing International Research Network of Mathematical Oncology (Fusion of Mathematics and Biology), Oct. 2020, English, Osaka, Domestic conferenceThe parameter variety of unidentifiable state-space models and its applications to analysis of biological systemsOral presentation
- 日本応用数理学会2020年度年会, Sep. 2020, Japanese, オンライン, Domestic conference分布系のカオス同期化とニューラルネットワークを用いた秘匿通信システムOral presentation
- 日本応用数理学会2020年度年会, Sep. 2020, Japanese, オンライン, Domestic conference常微分方程式モデルの学習における離散化手法の影響についてOral presentation
- 第25回計算工学講演会, Jun. 2020, Japanese, オンライン, Domestic conferenceピアノの弦と駒の連成シミュレーションによるエネルギー移動の可視化Oral presentation
- 日本応用数理学会第１６回研究部会連合発表会, Mar. 2020, 東京, Domestic conference波動方程式と弾性方程式からなる連成系のシンプレクティッ ク性についてOral presentation
- 日本応用数理学会第１６回研究部会連合発表会, Mar. 2020, 東京, Domestic conference時間方向対称性を利用した2つのエネルギー保存数値解法の等価条件についてOral presentation
- 日本応用数理学会第１６回研究部会連合発表会, Mar. 2020, 東京, Domestic conference微分代数の応用に向けた多項式常微分方程式モデルの簡約Oral presentation
- Workshop: シミュレーションとモデリングのための計算代数 2020, Jan. 2020, 神戸, Domestic conference微分代数に基づく数理モデリングアプローチOral presentation
- Workshop: シミュレーションとモデリングのための計算代数 2020, Jan. 2020, 神戸, Domestic conference幾何学的離散力学と対称性 II[Invited]Oral presentation
- Workshop: シミュレーションとモデリングのための計算代数 2020, Jan. 2020, 神戸, Domestic conference幾何学的離散力学と対称性 I[Invited]Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第23回シンポジウム, Dec. 2019, 神戸, Domestic conference指数型分布族の定める多様体上の離散力学に基づく時系列モデルとネットワーク解析への応用Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第23回シンポジウム, Dec. 2019, 神戸, Domestic conference高頻度データに対する再帰型ニューラルネットモデルとその比較Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第23回シンポジウム, Dec. 2019, 神戸, Domestic conference波動方程式と弾性方程式の構造保存型連成数値計算Oral presentation
- 2019 年度応用数学合同研究集会, Dec. 2019, 滋賀, Domestic conference同定不可能モデルに対するパラメータ多様体による解析とその近似導出についてOral presentation
- 2019 年度応用数学合同研究集会, Dec. 2019, 滋賀, Domestic conference自動微分による離散力学とアルゴリズム的数値解析Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第22回シンポジウム, Dec. 2018, Japanese, 香川, Domestic conference微分代数方程式モデルのモデルパラメータと解に関するグレブナー基底を用いた解析Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第22回シンポジウム, Dec. 2018, Japanese, 香川, Domestic conference波動型偏微分方程式に対する幾何学的弱形式Oral presentation
- RIMS研究集会, Nov. 2018, Japanese, 京都, Domestic conferenceアレルギー疾患の個別化医療に向けた抗原・抗体の体内動態シミュレーションOral presentation
- 情報計算科学生物学会2018年大会, Oct. 2018, Japanese, 東京, Domestic conferenceModeling and simulations of the kinetics of antigens and antibodies towards personalized medicine for allergiesOral presentation
- 日本応用数理学会2018年度年会, Sep. 2018, Japanese, 愛知, Domestic conference統計多様体上の状態空間モデルを用いた発展型ネットワーク解析Oral presentation
- 日本応用数理学会2018年度年会, Sep. 2018, Japanese, 愛知, Domestic conference抗原・抗体の体内動態の定量的解析に向けたモデルパラメータの多様性に対する考察Oral presentation
- 日本応用数理学会2018年度年会, Sep. 2018, Japanese, 愛知, Domestic conferenceアンケートデータを用いた交流ネットワーク推定手法Oral presentation
- MIMS現象数理学研究拠点共同研究集会「幾何的解析と形状表現の数理」, Aug. 2018, Japanese, 東京, Domestic conference情報幾何学を用いた発展型ネットワークモデルに基づく相転移に着目した異常検知の試みOral presentation
- SIAM Conference on the Life Science (LS18), Aug. 2018, English, Minneapolis, Domestic conferenceParameters of Models using Dynamical Systems with Conservation LawsOral presentation
- SIAM Conference on the Life Science (LS18), Aug. 2018, English, Minneapolis, Domestic conferenceModeling the Kinetics of Antigens and Antibodies for Analysis of the Mechanism of AllergyOral presentation
- Data Science, Statistics & Visualisation (DSSV 2018), Jul. 2018, English, Wien, Domestic conferenceParameter estimation for compartment models of biological systemsOral presentation
- Data Science, Statistics & Visualisation (DSSV 2018), Jul. 2018, English, Wien, Domestic conferenceAutoregressive models on statistical Riemannian manifolds for analysis of evolutionary networksOral presentation
- The 13th World Congress in Computational Mechanics, Jul. 2018, English, New York, Domestic conferenceApplication of Hamiltonian Flows to Exploring Parameters of Mathematical Models in Situations with Insufficient DataOral presentation
- 第47回数値解析シンポジウム, Jun. 2018, Japanese, 福井, Domestic conference体内動態に対するコンパートメントモデルのモデルパラメータ推定手法についてOral presentation
- 第47回数値解析シンポジウム, Jun. 2018, Japanese, 福井, Domestic conference潜在変数ネットワークモデルを用いた放牧牛の交流ネットワーク解析Oral presentation
- 第47回数値解析シンポジウム, Jun. 2018, Japanese, 福井, Domestic conferenceあるテーマパークにおける地形的集客効果の感度分析Oral presentation
- 日本応用数理学会第14回研究部会連合発表会, Mar. 2018, Japanese, Domestic conference有限要素外積解析に対するRRGMRES法Oral presentation
- 日本数学会2018年度年会, Mar. 2018, Japanese, Domestic conference変分原理に基づくエネルギー保存数値解法の Lie 群上への拡張Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第21回シンポジウム, Mar. 2018, Japanese, Domestic conference統計多様体上のARモデルを用いた発展型ネットワーク解析Oral presentation
- 応用数理 学生・若手研究者のための研究交流会, Mar. 2018, Japanese, Domestic conference質点ばね系を用いたレザバーコンピューティングの数値実験Oral presentation
- 日本応用数理学会第14回研究部会連合発表会, Mar. 2018, Japanese, Domestic conferenceアレルギー発症メカニズムの解析に向けた抗原・抗体の体内動態モデルの構築, 及び, Husbyらの実験データに対するパラメータ推定とその考察Oral presentation
- 日本応用数理学会環瀬戸内応用数理研究部会第21回シンポジウム, Mar. 2018, Japanese, Domestic conferenceアレルギー発症シミュレーションに向けた生理学的薬物動態モデルの応用Oral presentation
- SIAM Conference on Parallel Processing for Scientific Computing, Mar. 2018, English, International conferenceEnergy-Preserving Parareal Algorithm for the Hamilton EquationNominated symposium
- 第46回数値解析シンポジウム, 2017, Japanese, Domestic conference離散偏導関数法と数値積分の併用Oral presentation
- 第46回数値解析シンポジウム, 2017, Japanese, Domestic conference離散外積解析における離散 Hodge スター作用素の誤差評価Oral presentation
- 日本応用数理学会2017年度年会, 2017, Japanese, Domestic conference離散外積解析から導かれる有限積分法のマルチシンプレクティック性についてOral presentation
- 第46回数値解析シンポジウム, 2017, Japanese, Domestic conference速度比例減衰項をもつ系に対する変分原理を利用した数値解法とその比較Oral presentation
- 日本応用数理学会2017年度年会, 2017, Japanese, Domestic conference指数ランダムグラフモデルに基づくネットワークに対するARモデルPoster presentation
- the International Conference on Scientific Computation And Differential Equations 2017 (SciCADE 2017), 2017, English, International conferenceRegression model on statistical manifolds and its application to evolutionary network analysisOral presentation
- the International Conference on Scientific Computation And Differential Equations 2017 (SciCADE 2017), 2017, English, International conferenceDiscrete partial derivative method with numerical integrationsOral presentation
- 2017年度応用数学合同研究集会, 2017, Japanese, Domestic conferencecurl-curl型偏微分方程式に対する有限要素外積解析の応用Oral presentation
- the International Conference on Scientific Computation And Differential Equations 2017 (SciCADE 2017), 2017, English, International conferenceAutomatic discrete differentiation and its applicationsOral presentation
- 日本応用数理学会2016年度年会, Sep. 2016, Japanese, Domestic conference変分原理に基づくエネルギー保存数値解法の一般のHamilton系への拡張Oral presentation
- 研究集会「常微分方程式の数値解法とその周辺2016」, Jul. 2016, Japanese, Domestic conference離散化した heavy-ball-with-friction method のパラメータについてOral presentation
- 第45回数値解析シンポジウム, Jun. 2016, Japanese, Domestic conference波動方程式に対するシンプレクティックかつエネルギー保存スキームについてOral presentation
- 第45回数値解析シンポジウム, Jun. 2016, Japanese, Domestic conference散逸型偏微分方程式に対するある種の変分原理に基づく散逸スキームの導出法Oral presentation
- 第45回数値解析シンポジウム, Jun. 2016, Japanese, Domestic conference曲面上の熱方程式に対する散逸性保存型数値解法の導出と評価Oral presentation
- 第45回数値解析シンポジウム, Jun. 2016, Japanese, Domestic conferenceWebster方程式に対するある数値解法の長時間挙動についてOral presentation
- 日本応用数理学会第12回研究部会連合発表会, Mar. 2016, Japanese, 神戸学院大学, Domestic conference地域コミュニティ構造の変化と改善に対する統計解析手法Oral presentation
- 日本応用数理学会研究部会連合発表会, Mar. 2016, Japanese, 神戸学院大学, Domestic conference自動離散微分とその応用Oral presentation
- 日本数学会2016年度年会, Mar. 2016, Japanese, 筑波大学, Domestic conference散逸型構造保存型数値解法の多層パーセプトロン学習法への応用Oral presentation
- 応用数学合同研究集会, Dec. 2015, Japanese, 龍谷大学, Domestic conference地域コミュニティの構造変化に対する検定理論Oral presentation
- 研究会「数理構造保存を接点とした数学・HPC・実科学のクロスオーバー」, Dec. 2015, Japanese, 電気通信大学, Domestic conferenceCaldirola-Kanai型変分原理に基づく構造保存型数値解法と多層パーセプトロン学習法への応用についてOral presentation
- 日本応用数理学会2015年度年会, Sep. 2015, Japanese, 金沢大学, Domestic conference対称性を利用した離散勾配法におけるLegendre変換に関する考察[Invited]Invited oral presentation
- 日本応用数理学会2015年度年会, Sep. 2015, Japanese, 金沢大学, Domestic conferenceハミルトン方程式に対する時間対称性を用いた離散勾配スキームの導出法Oral presentation
- 日本応用数理学会2015年度年会, Sep. 2015, Japanese, 金沢大学, Domestic conferenceシンプレクティック数値積分法による力学的摂動Oral presentation
- 日本応用数理学会2015年度年会, Sep. 2015, Japanese, 金沢大学, Domestic conferenceある種の散逸型微分方程式に対する構造保存型数値解法Oral presentation
- the International Conference on Scientific Computation And Differential Equations 2015 (SciCADE 2015), Sep. 2015, English, University of Potsdam, International conferenceStructure-preserving method for a certain class of dissipative differential equationsOral presentation
- the International Conference on Scientific Computation And Differential Equations 2015 (SciCADE 2015), Sep. 2015, English, University of Potsdam, International conferenceEnergy-preserving discrete gradient schemes for the Hamilton equation based on the variational principleOral presentation
- Computational and Geometric Approaches for Nonlinear Phenomena, Aug. 2015, English, 早稲田大学, International conferenceNumerical integrations that preserve energy behaviors using the variational principleOral presentation
- Recent developments in numerical analysis with special emphasis on complex analysis, Jul. 2015, English, 東京大学, International conferenceStructure-preserving numerical integrators for the KdV equation using an almost complex structureOral presentation
- 日本老年社会科学会第57回大会, Jun. 2015, Japanese, Domestic conference地域高齢者を対象とした健康教室による参加者間交流ネットワーク形成に関する研究Poster presentation
- 第44回数値解析シンポジウム, Jun. 2015, Japanese, ぶどうの丘, Domestic conference大規模ネットワークにおける複数ノード組に対する重要度の特徴付けOral presentation
- 第44回数値解析シンポジウム, Jun. 2015, Japanese, ぶどうの丘, Domestic conferenceピアノの物理モデルとその効率的な数値計算法の検討Oral presentation
- 第44回数値解析シンポジウム, Jun. 2015, Japanese, ぶどうの丘, Domestic conferenceL2射影を用いた離散偏導関数法による弦のサウンドレンダリングOral presentation
- 日本応用数理学会研究部会連合発表会, Mar. 2015, Japanese, 東京, Domestic conference測地線方程式に対する離散勾配法の適用とアインシュタイン方程式の数値解を用いるための基礎検討Oral presentation
- 日本数学会２０１４年度年会, Mar. 2014, Japanese, 東京, Domestic conference境界付き多様体上における有限要素外積解析の弱形式の適切性についてOral presentation
- 研究集会「常微分方程式の数値解法とその周辺2014」, Mar. 2014, Japanese, 静岡, Domestic conference楽器シミュレーションに対する構造保存型数値解法の応用と関連する数理的課題Oral presentation
- RIMS研究集会「新時代の科学技術を牽引する数値解析学」, 2014, Japanese, 京都, Domestic conference離散勾配法のRiemann構造不変性とシンプレクティック幾何学的再構築[Invited]Invited oral presentation
- 応用数学合同研究集会, 2014, Japanese, 滋賀, Domestic conference数値相対論のための測地線方程式に対する構造保存型数値解法の適用Oral presentation
- 第３回岐阜数理科学研究会, 2014, Japanese, 岐阜, Domestic conference幾何学的構造保存型数値解法に対する力学理論的アプローチ[Invited]Invited oral presentation
- 第４３回数値解析シンポジウム, 2014, Japanese, 沖縄, Domestic conference異なる内積により得られる Webster 方程式の２つのハミルトン構造Oral presentation
- 日本応用数理学会2014年度年会, 2014, Japanese, 東京, Domestic conference異なるRiemann構造をもつWebster方程式に対する離散変分導関数法の不変性Oral presentation
- 日本学術会議第4回計算力学シンポジウム, 2014, Japanese, 東京, Domestic conferenceハミルトン偏微分方程式に対する構造保存型数値解法[Invited]Invited oral presentation
- 第４３回数値解析シンポジウム, 2014, Japanese, 沖縄, Domestic conferenceシンプレクティック法による摂動を用いた太陽系の安定性検証Oral presentation
- 応用数学合同研究集会, 2014, Japanese, 滋賀, Domestic conferenceシンプレクティック空間上の離散勾配法Oral presentation
- 日本応用数理学会2014年度年会, 2014, Japanese, 東京, Domestic conferenceグラフに対するOllivier-Ricci曲率の数値計算Oral presentation
- Foundations of Computational Mathematics Conference 2014, 2014, English, ウルグアイ, International conferenceSimulation of Wind Instruments and a Geometric Invariance of the Discrete Gradient Method[Invited]Invited oral presentation
- 流体方程式の構造と特異性に迫る数値解析・数値計算, 2014, English, 愛知, International conferenceOn the well-posedness of the weak form of the finite element exterior calculus on manifolds[Invited]Invited oral presentation
- 2nd International Workshop on Numerical Linear Algebra and Its Applications, 2014, English, 中国, International conferenceApplication of Structure-Preserving Numerical Methods to Simulation of Musical Instruments[Invited]Invited oral presentation
- 日本数学会 秋季総合分科会, Sep. 2013, Japanese, 愛媛, Domestic conference有限要素外積解析に基づく波動型方程式に対するエネルギー保存型数値解法[Invited]Invited oral presentation
- 日本応用数理学会 2013 年度年会, Sep. 2013, Japanese, 福岡, Domestic conferenceホロノーム拘束をもつハミルトン系に対する離散勾配法Oral presentation
- 日本応用数理学会 2013 年度年会, Sep. 2013, Japanese, 福岡, Domestic conferenceシンプレクティック数値積分法における修正ハミルトニアンの存在定理についてOral presentation
- the International Conference on Scientific Computation And Differential Equations 2013 (SciCADE 2013), Sep. 2013, English, Valladolid, Spain, International conferenceLagrangian approach of the discrete gradient method based on finite element methodsOral presentation
- ワークショップ「有限体積法の数学的基盤理論の確立III」, Aug. 2013, Japanese, 愛媛, Domestic conferenceシンプレクティックフローとしてのシンプレクティック数値積分法Oral presentation
- 2013 Tokyo Workshop on Structure-Preserving Methods, Jan. 2013, English, Tokyo, International conferenceOn the finite element exterior calculus for parabolic equationsOral presentation
- 応用数学合同研究集会, Dec. 2012, Japanese, 滋賀, Domestic conference放物型方程式に対する有限要素外積解析の誤差評価についてOral presentation
- 10th International Conference of Numerical Analysis and Applied Mathematics, Sep. 2012, English, Greece, International conferenceApplication of the Lagrangian Approach of the Discrete Gradient Method to Scleronomic Holonomic SystemsOral presentation
- 日本応用数理学会 2012年度年会, Aug. 2012, Japanese, 北海道, Domestic conferenceホロノミック系に対するラグランジュ力学的離散勾配法Oral presentation
- 15th International Congress on Computational and Applied Mathematics, Jul. 2012, English, Gent, Belgium, International conferenceA Lagrangian Approach to Deriving Local-Energy-Preserving Numerical Schemes for the Euler-Lagrange Partial Differential EquationsOral presentation
- 有限体積法の数学的基盤理論の確立II, Mar. 2012, Japanese, 福岡, Domestic conferenceラグランジュ力学に基づく保存型数値解法導出法とその応用Oral presentation
- 常微分方程式の数値解法とその周辺 2012, Mar. 2012, Japanese, 静岡, Domestic conferenceNewton法の Parareal Algorithm による並列化Oral presentation
- 日本応用数理学会研究部会連合発表会, Mar. 2012, Japanese, 福岡, Domestic conferenceEuler-Lagrange 偏微分方程式に対する局所エネルギー保存スキーム導出法Oral presentation
- 2012 Tokyo Workshop on Structure-Preserving Methods, Jan. 2012, English, Tokyo, International conferenceBackward Error Analysis of the Scheme for the KdV Equation by the Discrete Variational Derivative MethodOral presentation
- 応用数学合同研究集会, Dec. 2011, Japanese, 瀬田, Domestic conferenceKdV 方程式に対するある半離散スキームの後退誤差解析Oral presentation
- 日本応用数理学会「行列・固有値問題の解法とその応用」研究部会第12回研究会, Nov. 2011, Japanese, 日本応用数理学会, 東京, Domestic conference時間依存固有値問題の数値解法に関する基礎検討Oral presentation
- RIMS研究集会「科学技術計算における理論と応用の新展開」, Oct. 2011, Japanese, 京都, Domestic conferenceある半離散スキームによるソリトンのシミュレーションについてOthers
- 日本応用数理学会 2011年度年会, Sep. 2011, Japanese, 京都, Domestic conference変分構造をもつ楕円型方程式に対する離散勾配法の応用Oral presentation
- International Workshop on Numerical Linear Algebra and Its Applications, Jul. 2011, English, China, International conferenceThe Discrete Variational Derivative Method Based on Discrete Differential FormsInvited oral presentation
- the International Conference on Scientific Computation And Differential Equations 2011 (SciCADE 2011), Jul. 2011, English, Canada, International conferenceA Lagrangian Approach to Deriving Energy-Preserving Numerical Schemes for the Euler-Lagrange Partial Differential Equations and Its Applications[Invited]Invited oral presentation
- Institute of Electrical and Electronics EngineersMay 2020 - Present
- 情報処理学会Feb. 2020 - Present
- Society for Industrial and Applied MathematicsJan. 2017 - Present
- American Institute of Aeronautics and Astronautics
- 日本流体力学会
- Mathematical Association of America
- 日本数学会
- 日本応用数理学会
- Japan Science and Technology Agency, Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE), ASPIRE for Rising Scientists, Kobe University, Jan. 2024 - Mar. 2027Deep Scientific Computing: integration of physical structure and deep learning through mathematical science
- Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), Kobe University, 01 Apr. 2022 - 31 Mar. 2025A longitudinal study of age-related changes in emotion regulation and trust and their relationship to social connectedness
- Japan Science and Technology Agency, Strategic Basic Research Programs (CREST), Oct. 2019 - Mar. 2025, Principal investigatorStructure Preserving System Modeling and Simulation Basis Based on Geometric Discrete Mechanics
- 日本学術振興会, 科学研究費助成事業 基盤研究(C), 基盤研究(C), 神戸大学, 01 Apr. 2020 - 31 Mar. 2024ブラックボックス微分方程式モデルに対する保存則抽出手法とネットワーク解析への応用2021年度は，主に，与えられた時系列データの背後に潜むシンプレクティック構造を抽出する手法の開発に取り組んだ．実際の問題に現れる，保存量をもつ微分方程式の多くはハミルトン方程式であるが，ハミルトン方程式はシンプレクティック多様体上で，エネルギー関数が定めるフローとして定義される．シンプレクティック多様体は，シンプレクティック形式と呼ばれる微分2形式をもつ多様体であるが，これは，一般には，状態変数に依存してよい量であり，データから学習することが必要である． 一般に，微分2形式は歪対称行列に対応するため，素朴な手法としては，データから歪対称行列を学習する手法が考えられる．しかし，実際には，シンプレクティック形式は閉形式である必要もあり，単に歪対称行列を学習するだけでは，シンプレクティック形式に対応するとは限らない． 本研究では，de Rhamコホモロジーを考慮すると，多くのシンプレクティック多様体上で，シンプレクティック形式が微分1形式の外微分によって導かれることに着目した．具体的には，微分2形式を直接学習するのではなく，それを導く微分1形式をデータから学習することで，シンプレクティック形式以外に対応しない歪対称行列が学習されることを防ぐ手法を構築した． この手法を用いれば，与えられたデータに隠されたシンプレクティック構造を抽出することが可能となり，隠された運動方程式を発見することが出来るようになる．また，提案手法は，ハミルトン方程式の幾何学的な性質，特に座標変換不変性を利用しており，データがどのように表現されていたとしても，方程式を学習することが可能である．そのため，データの前処理とも相性が良く，この性質は，今後，様々な形で応用できる可能性がある．
- 科学研究費補助金／挑戦的研究（開拓）, Jun. 2018 - Mar. 2021Competitive research funding
- 国立研究開発法人科学技術振興機構, 戦略的創造研究推進事業（さきがけ）, Oct. 2016 - Mar. 2020, Principal investigator情報幾何学と離散力学の融合と社会ネットワーク解析への応用Competitive research funding
- 学術研究助成基金助成金／基盤研究(C), Apr. 2014 - Mar. 2019, Principal investigatorCompetitive research funding
- 科学研究費補助金／基盤研究(B), Apr. 2015 - Mar. 2018Competitive research funding
- Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Scientific Research (B), The University of Tokyo, 01 Apr. 2011 - 31 Mar. 2015This research project was aimed at development and application of the mathematical theory for the finite volume method that is a popular structure-preserving discretization method. From the mathematical stand-point, the discrete Sobolev inequality, interpolation error constants, discrete Rellich's theorem, discrete maximum principle, and discrete differential form were studied and many useful results were obtained. As an important application, results were applied to analysis of the finite volume method for the mathematical model describes the aggregation of slime molds resulting from their chemotactic features. In particular, the proof of the existence of a discrete free energy was succeeded. Another important application was an extension of energy-preserving numerical method based on Lagrange mechanics to the finite volume method by using the theory of the discrete differential form.
- 科学研究費補助金／若手研究(B), 2011, Principal investigatorCompetitive research funding
- Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B), Grant-in-Aid for Young Scientists (B), The University of Tokyo, 2007 - 2009Nonreflecting boundary conditions are of importance in numerical simulations of compressive fluid. In this research I developed a new nonreflecting boundary condition based on the Riemann invariant manifold. An improvement of this boundary condition was found to be same as Thompson's boundary condition, and this provided a new derivation of Thompson's boundary condition and a stability analysis. Researches on the discrete variational method are also performed in order to stabilize this boundary condition. As a result, some extensions of the discrete variational method are achieved.