Voronoi Random Fields. - Takaharu Yaguchi (ISVD, 2006)

Conservative numerical schemes for the Ostrovsky equation. - Takaharu Yaguchi; Takayasu Matsuo; Masaaki Sugihara (J. Comput. Appl. Math., 2010)

An extension of the discrete variational method to nonuniform grids. - Takaharu Yaguchi; Takayasu Matsuo; Masaaki Sugihara (J. Comput. Phys., 2010)

A multi-symplectic integration of the Ostrovsky equation. - Yuto Miyatake; Takaharu Yaguchi; Takayasu Matsuo (JSIAM Lett., 2011)

Numerical integration of the Ostrovsky equation based on its geometric structures. - Yuto Miyatake; Takaharu Yaguchi; Takayasu Matsuo (J. Comput. Phys., 2012)

The discrete variational derivative method based on discrete differential forms. - Takaharu Yaguchi; Takayasu Matsuo; Masaaki Sugihara (J. Comput. Phys., 2012)

A conservative compact finite difference scheme for the KdV equation. - Hiroki Kanazawa; Takayasu Matsuo; Takaharu Yaguchi (JSIAM Lett., 2012)

Geometric investigation of the discrete gradient method for the Webster equation with a weighted inner product. - Ai Ishikawa; Takaharu Yaguchi (JSIAM Lett., 2015)

Application of the variational principle to deriving energy-preserving schemes for the Hamilton equation. - Ai Ishikawa; Takaharu Yaguchi (JSIAM Lett., 2016)

Mass-Spring Damper Array as a Mechanical Medium for Computation. - Yuki Yamanaka; Takaharu Yaguchi; Kohei Nakajima; Helmut Hauser (ICANN (3), 2018)

Automatic discrete differentiation and its applications. - Ai Ishikawa; Takaharu Yaguchi (CoRR, 2019)

Deep Energy-based Modeling of Discrete-Time Physics. - Takashi Matsubara 001; Ai Ishikawa; Takaharu Yaguchi (NeurIPS, 2020)

Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis. - Mizuka Komatsu; Takaharu Yaguchi (CoRR, 2020)

Error Factor Analysis of DNN-based Fingerprinting Localization through Virtual Space. - Takuto Jikyo; Tomio Kamada; Chikara Ohta; Takaharu Yaguchi; Kenji Oyama; Takenao Ohkawa; Ryo Nishide (CCNC, 2021)

Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems. - Yuhan Chen; Takashi Matsubara 001; Takaharu Yaguchi (NeurIPS, 2021)

Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory. - Takashi Matsubara 001; Yuto Miyatake; Takaharu Yaguchi (NeurIPS, 2021)

Universal Approximation Properties of Neural Networks for Energy-Based Physical Systems. - Yuhan Chen; Takashi Matsubara 001; Takaharu Yaguchi (CoRR, 2021)

Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics. - Nobuki Takayama; Takaharu Yaguchi; Yi Zhang (CoRR, 2021)

An Error Analysis Framework for Neural Network Modeling of Dynamical Systems. - Shunpei Terakawa; Takashi Matsubara 001; Takaharu Yaguchi (CoRR, 2021)

Secret Communication Systems Using Chaotic Wave Equations with Neural Network Boundary Conditions. - Yuhan Chen; Hideki Sano; Masashi Wakaiki; Takaharu Yaguchi (Entropy, 2021)

Algebraic approach towards the exploitation of "softness": the input-output equation for morphological computation. - Mizuka Komatsu; Takaharu Yaguchi; Kohei Nakajima (Int. J. Robotics Res., 2021)

KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss. - Yuhan Chen; Takashi Matsubara 001; Takaharu Yaguchi (AAAI, 2022)

Causal inference for empirical dynamical systems based on persistent homology. - Hiroaki Bando; Shizuo Kaji; Takaharu Yaguchi (JSIAM Lett., 2022)

Symplecticity of coupled Hamiltonian systems. - Shunpei Terakawa; Takaharu Yaguchi (JSIAM Lett., 2022)

FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities. - Takashi Matsubara 001; Takaharu Yaguchi (ICLR, 2023)

Good Lattice Training: Physics-Informed Neural Networks Accelerated by Number Theory. - Takashi Matsubara 001; Takaharu Yaguchi (CoRR, 2023)

Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs. - Yusuke Tanaka 002; Takaharu Yaguchi; Tomoharu Iwata; Naonori Ueda (CoRR, 2024)

Loss Function for Deep Learning to Model Dynamical Systems. - Takahito Yoshida; Takaharu Yaguchi; Takashi Matsubara 001 (IEICE Trans. Inf. Syst., 2024)

The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations. - Takashi Matsubara 001; Yuto Miyatake; Takaharu Yaguchi (IEEE Trans. Neural Networks Learn. Syst., 2024)

Number Theoretic Accelerated Learning of Physics-Informed Neural Networks. - Takashi Matsubara 001; Takaharu Yaguchi (AAAI, 2025)

Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations. - Yusuke Tanaka 002; Takaharu Yaguchi; Tomoharu Iwata; Naonori Ueda (AISTATS, 2025)

Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains. - Razmik Arman Khosrovian; Takaharu Yaguchi; Hiroaki Yoshimura; Takashi Matsubara 001 (ICLR, 2025)

Learning Hamiltonian Density Using DeepONet. - Baige Xu; Yusuke Tanaka 002; Takashi Matsubara 001; Takaharu Yaguchi (CoRR, 2025)

Deep Energy-Based Discrete-Time Physical Model for Reproducing Energetic Behavior. - Takashi Matsubara 001; Takehiro Aoshima; Ai Ishikawa; Takaharu Yaguchi (IEEE Trans. Neural Networks Learn. Syst., 2025)