Takaharu Yaguchi

38 papers A* 7A 1Journal 27Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Deep Energy-Based Discrete-Time Physical Model for Reproducing Energetic Behavior. IEEE Trans. Neural Networks Learn. Syst. Takashi Matsubara, Takehiro Aoshima, Ai Ishikawa, Takaharu Yaguchi
2025	A	`conf`	Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations. AISTATS Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda
2025	J	`jnl`	Learning Hamiltonian Density Using DeepONet. CoRR Baige Xu, Yusuke Tanaka, Takashi Matsubara, Takaharu Yaguchi
2025	A*	`conf`	Number Theoretic Accelerated Learning of Physics-Informed Neural Networks. AAAI Takashi Matsubara, Takaharu Yaguchi
2025	A*	`conf`	Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains. ICLR Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, Takashi Matsubara
2024	J	`jnl`	Loss Function for Deep Learning to Model Dynamical Systems. IEICE Trans. Inf. Syst. Takahito Yoshida, Takaharu Yaguchi, Takashi Matsubara
2024	J	`jnl`	Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs. CoRR Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda
2024	J	`jnl`	Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains. CoRR Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, Takashi Matsubara
2024	J	`jnl`	The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations. IEEE Trans. Neural Networks Learn. Syst. Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi
2023	A*	`conf`	FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities. ICLR Takashi Matsubara, Takaharu Yaguchi
2023	J	`jnl`	Good Lattice Training: Physics-Informed Neural Networks Accelerated by Number Theory. CoRR Takashi Matsubara, Takaharu Yaguchi
2022	J	`jnl`	Causal inference for empirical dynamical systems based on persistent homology. JSIAM Lett. Hiroaki Bando, Shizuo Kaji, Takaharu Yaguchi
2022	J	`jnl`	FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities. CoRR Takashi Matsubara, Takaharu Yaguchi
2022	A*	`conf`	KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss. AAAI Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi
2022	J	`jnl`	Symplecticity of coupled Hamiltonian systems. JSIAM Lett. Shunpei Terakawa, Takaharu Yaguchi
2021	J	`jnl`	Algebraic approach towards the exploitation of "softness": the input-output equation for morphological computation. Int. J. Robotics Res. Mizuka Komatsu, Takaharu Yaguchi, Kohei Nakajima
2021	J	`jnl`	An Error Analysis Framework for Neural Network Modeling of Dynamical Systems. CoRR Shunpei Terakawa, Takashi Matsubara, Takaharu Yaguchi
2021	J	`jnl`	Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics. CoRR Nobuki Takayama, Takaharu Yaguchi, Yi Zhang
2021	—	`conf`	Error Factor Analysis of DNN-based Fingerprinting Localization through Virtual Space. CCNC Takuto Jikyo, Tomio Kamada, Chikara Ohta, Takaharu Yaguchi, Kenji Oyama, Takenao Ohkawa, Ryo Nishide
2021	A*	`conf`	Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems. NeurIPS Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi
2021	J	`jnl`	Secret Communication Systems Using Chaotic Wave Equations with Neural Network Boundary Conditions. Entropy Yuhan Chen, Hideki Sano, Masashi Wakaiki, Takaharu Yaguchi
2021	A*	`conf`	Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory. NeurIPS Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi
2021	J	`jnl`	Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory. CoRR Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi
2021	J	`jnl`	Symplecticity of coupled Hamiltonian systems. CoRR Shunpei Terakawa, Takaharu Yaguchi
2021	J	`jnl`	Universal Approximation Properties of Neural Networks for Energy-Based Physical Systems. CoRR Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi
2020	A*	`conf`	Deep Energy-based Modeling of Discrete-Time Physics. NeurIPS Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi
2020	J	`jnl`	Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis. CoRR Mizuka Komatsu, Takaharu Yaguchi
2019	J	`jnl`	Automatic discrete differentiation and its applications. CoRR Ai Ishikawa, Takaharu Yaguchi
2018	—	`conf`	Mass-Spring Damper Array as a Mechanical Medium for Computation. ICANN (3) Yuki Yamanaka, Takaharu Yaguchi, Kohei Nakajima, Helmut Hauser
2016	J	`jnl`	Application of the variational principle to deriving energy-preserving schemes for the Hamilton equation. JSIAM Lett. Ai Ishikawa, Takaharu Yaguchi
2015	J	`jnl`	Geometric investigation of the discrete gradient method for the Webster equation with a weighted inner product. JSIAM Lett. Ai Ishikawa, Takaharu Yaguchi
2012	J	`jnl`	A conservative compact finite difference scheme for the KdV equation. JSIAM Lett. Hiroki Kanazawa, Takayasu Matsuo, Takaharu Yaguchi
2012	J	`jnl`	Numerical integration of the Ostrovsky equation based on its geometric structures. J. Comput. Phys. Yuto Miyatake, Takaharu Yaguchi, Takayasu Matsuo
2012	J	`jnl`	The discrete variational derivative method based on discrete differential forms. J. Comput. Phys. Takaharu Yaguchi, Takayasu Matsuo, Masaaki Sugihara
2011	J	`jnl`	A multi-symplectic integration of the Ostrovsky equation. JSIAM Lett. Yuto Miyatake, Takaharu Yaguchi, Takayasu Matsuo
2010	J	`jnl`	An extension of the discrete variational method to nonuniform grids. J. Comput. Phys. Takaharu Yaguchi, Takayasu Matsuo, Masaaki Sugihara
2010	J	`jnl`	Conservative numerical schemes for the Ostrovsky equation. J. Comput. Appl. Math. Takaharu Yaguchi, Takayasu Matsuo, Masaaki Sugihara
2006	—	`conf`	Voronoi Random Fields. ISVD Takaharu Yaguchi