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Takaharu Yaguchi
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2020 – today
- 2024
- [j14]Takahito Yoshida, Takaharu Yaguchi, Takashi Matsubara:
Loss Function for Deep Learning to Model Dynamical Systems. IEICE Trans. Inf. Syst. 107(11): 1458-1462 (2024) - [j13]Takashi Matsubara
, Yuto Miyatake
, Takaharu Yaguchi:
The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations. IEEE Trans. Neural Networks Learn. Syst. 35(8): 10526-10538 (2024) - [i11]Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda:
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs. CoRR abs/2402.09018 (2024) - [i10]Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, Takashi Matsubara:
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains. CoRR abs/2410.11480 (2024) - 2023
- [c8]Takashi Matsubara, Takaharu Yaguchi:
FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities. ICLR 2023 - [i9]Takashi Matsubara, Takaharu Yaguchi:
Good Lattice Training: Physics-Informed Neural Networks Accelerated by Number Theory. CoRR abs/2307.13869 (2023) - 2022
- [j12]Shunpei Terakawa, Takaharu Yaguchi:
Symplecticity of coupled Hamiltonian systems. JSIAM Lett. 14: 37-40 (2022) - [j11]Hiroaki Bando, Shizuo Kaji, Takaharu Yaguchi:
Causal inference for empirical dynamical systems based on persistent homology. JSIAM Lett. 14: 69-72 (2022) - [c7]Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi:
KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss. AAAI 2022: 6322-6332 - [i8]Takashi Matsubara, Takaharu Yaguchi
:
FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities. CoRR abs/2210.00272 (2022) - 2021
- [j10]Yuhan Chen
, Hideki Sano
, Masashi Wakaiki
, Takaharu Yaguchi
:
Secret Communication Systems Using Chaotic Wave Equations with Neural Network Boundary Conditions. Entropy 23(7): 904 (2021) - [j9]Mizuka Komatsu, Takaharu Yaguchi
, Kohei Nakajima:
Algebraic approach towards the exploitation of "softness": the input-output equation for morphological computation. Int. J. Robotics Res. 40(1) (2021) - [c6]Takuto Jikyo, Tomio Kamada
, Chikara Ohta, Takaharu Yaguchi
, Kenji Oyama, Takenao Ohkawa, Ryo Nishide:
Error Factor Analysis of DNN-based Fingerprinting Localization through Virtual Space. CCNC 2021: 1-4 - [c5]Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi:
Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems. NeurIPS 2021: 16659-16670 - [c4]Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi:
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory. NeurIPS 2021: 20772-20784 - [i7]Takashi Matsubara, Yuto Miyatake, Takaharu Yaguchi:
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory. CoRR abs/2102.09750 (2021) - [i6]Yuhan Chen, Takashi Matsubara, Takaharu Yaguchi:
Universal Approximation Properties of Neural Networks for Energy-Based Physical Systems. CoRR abs/2102.11923 (2021) - [i5]Nobuki Takayama, Takaharu Yaguchi, Yi Zhang:
Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics. CoRR abs/2111.10947 (2021) - [i4]Shunpei Terakawa, Takaharu Yaguchi:
Symplecticity of coupled Hamiltonian systems. CoRR abs/2112.13589 (2021) - [i3]Shunpei Terakawa, Takashi Matsubara, Takaharu Yaguchi:
An Error Analysis Framework for Neural Network Modeling of Dynamical Systems. CoRR abs/2112.14014 (2021) - 2020
- [c3]Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi:
Deep Energy-based Modeling of Discrete-Time Physics. NeurIPS 2020 - [i2]Mizuka Komatsu, Takaharu Yaguchi:
Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis. CoRR abs/2012.11906 (2020)
2010 – 2019
- 2019
- [i1]Ai Ishikawa, Takaharu Yaguchi:
Automatic discrete differentiation and its applications. CoRR abs/1905.08604 (2019) - 2018
- [c2]Yuki Yamanaka
, Takaharu Yaguchi
, Kohei Nakajima
, Helmut Hauser
:
Mass-Spring Damper Array as a Mechanical Medium for Computation. ICANN (3) 2018: 781-794 - 2016
- [j8]Ai Ishikawa, Takaharu Yaguchi:
Application of the variational principle to deriving energy-preserving schemes for the Hamilton equation. JSIAM Lett. 8: 53-56 (2016) - 2015
- [j7]Ai Ishikawa, Takaharu Yaguchi:
Geometric investigation of the discrete gradient method for the Webster equation with a weighted inner product. JSIAM Lett. 7: 17-20 (2015) - 2012
- [j6]Takaharu Yaguchi
, Takayasu Matsuo, Masaaki Sugihara:
The discrete variational derivative method based on discrete differential forms. J. Comput. Phys. 231(10): 3963-3986 (2012) - [j5]Yuto Miyatake
, Takaharu Yaguchi
, Takayasu Matsuo:
Numerical integration of the Ostrovsky equation based on its geometric structures. J. Comput. Phys. 231(14): 4542-4559 (2012) - [j4]Hiroki Kanazawa, Takayasu Matsuo, Takaharu Yaguchi:
A conservative compact finite difference scheme for the KdV equation. JSIAM Lett. 4: 5-8 (2012) - 2011
- [j3]Yuto Miyatake
, Takaharu Yaguchi, Takayasu Matsuo:
A multi-symplectic integration of the Ostrovsky equation. JSIAM Lett. 3: 41-44 (2011) - 2010
- [j2]Takaharu Yaguchi
, Takayasu Matsuo, Masaaki Sugihara:
Conservative numerical schemes for the Ostrovsky equation. J. Comput. Appl. Math. 234(4): 1036-1048 (2010) - [j1]Takaharu Yaguchi
, Takayasu Matsuo, Masaaki Sugihara:
An extension of the discrete variational method to nonuniform grids. J. Comput. Phys. 229(11): 4382-4423 (2010)
2000 – 2009
- 2006
- [c1]Takaharu Yaguchi
:
Voronoi Random Fields. ISVD 2006: 66-75
Coauthor Index
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