Takaharu Yaguchi

Orcid: 0000-0001-9025-6015

According to our database1, Takaharu Yaguchi authored at least 28 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations.
IEEE Trans. Neural Networks Learn. Syst., August, 2024

Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs.
CoRR, 2024

2023
Good Lattice Training: Physics-Informed Neural Networks Accelerated by Number Theory.
CoRR, 2023

FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Symplecticity of coupled Hamiltonian systems.
JSIAM Lett., 2022

Causal inference for empirical dynamical systems based on persistent homology.
JSIAM Lett., 2022

KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Algebraic approach towards the exploitation of "softness": the input-output equation for morphological computation.
Int. J. Robotics Res., 2021

Secret Communication Systems Using Chaotic Wave Equations with Neural Network Boundary Conditions.
Entropy, 2021

An Error Analysis Framework for Neural Network Modeling of Dynamical Systems.
CoRR, 2021

Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics.
CoRR, 2021

Universal Approximation Properties of Neural Networks for Energy-Based Physical Systems.
CoRR, 2021

Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Error Factor Analysis of DNN-based Fingerprinting Localization through Virtual Space.
Proceedings of the 18th IEEE Annual Consumer Communications & Networking Conference, 2021

2020
Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis.
CoRR, 2020

Deep Energy-based Modeling of Discrete-Time Physics.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Automatic discrete differentiation and its applications.
CoRR, 2019

2018
Mass-Spring Damper Array as a Mechanical Medium for Computation.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

2016
Application of the variational principle to deriving energy-preserving schemes for the Hamilton equation.
JSIAM Lett., 2016

2015
Geometric investigation of the discrete gradient method for the Webster equation with a weighted inner product.
JSIAM Lett., 2015

2012
A conservative compact finite difference scheme for the KdV equation.
JSIAM Lett., 2012

The discrete variational derivative method based on discrete differential forms.
J. Comput. Phys., 2012

Numerical integration of the Ostrovsky equation based on its geometric structures.
J. Comput. Phys., 2012

2011
A multi-symplectic integration of the Ostrovsky equation.
JSIAM Lett., 2011

2010
An extension of the discrete variational method to nonuniform grids.
J. Comput. Phys., 2010

Conservative numerical schemes for the Ostrovsky equation.
J. Comput. Appl. Math., 2010

2006
Voronoi Random Fields.
Proceedings of the 3rd International Symposium on Voronoi Diagrams in Science and Engineering, 2006


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