Takaharu Yaguchi

CoRR, 2023

FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Symplecticity of coupled Hamiltonian systems.

[BibT_eX]

[DOI]

Shunpei Terakawa

JSIAM Lett., 2022

Causal inference for empirical dynamical systems based on persistent homology.

[BibT_eX]

[DOI]

Hiroaki Bando

Shizuo Kaji

JSIAM Lett., 2022

KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss.

[BibT_eX]

[DOI]

Yuhan Chen

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.

[BibT_eX]

[DOI]

Mizuka Komatsu

Kohei Nakajima

Int. J. Robotics Res., 2021

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

[BibT_eX]

[DOI]

Entropy, 2021

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

[BibT_eX]

[DOI]

Shunpei Terakawa

CoRR, 2021

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

[BibT_eX]

[DOI]

Nobuki Takayama

Yi Zhang

CoRR, 2021

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

[BibT_eX]

[DOI]

Yuhan Chen

CoRR, 2021

Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory.

[BibT_eX]

[DOI]

Yuto Miyatake

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.

[BibT_eX]

[DOI]

Yuhan Chen

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Mizuka Komatsu

CoRR, 2020

Deep Energy-based Modeling of Discrete-Time Physics.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Mass-Spring Damper Array as a Mechanical Medium for Computation.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

JSIAM Lett., 2016

2015

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

[BibT_eX]

[DOI]

JSIAM Lett., 2015

2012

A conservative compact finite difference scheme for the KdV equation.

[BibT_eX]

[DOI]

Hiroki Kanazawa

JSIAM Lett., 2012

The discrete variational derivative method based on discrete differential forms.

[BibT_eX]

[DOI]

Masaaki Sugihara

J. Comput. Phys., 2012

Numerical integration of the Ostrovsky equation based on its geometric structures.

[BibT_eX]

[DOI]

Yuto Miyatake

J. Comput. Phys., 2012

2011

A multi-symplectic integration of the Ostrovsky equation.

[BibT_eX]

[DOI]

Yuto Miyatake

JSIAM Lett., 2011

2010

An extension of the discrete variational method to nonuniform grids.

[BibT_eX]

[DOI]

Masaaki Sugihara

J. Comput. Phys., 2010

Conservative numerical schemes for the Ostrovsky equation.

[BibT_eX]

[DOI]

Masaaki Sugihara

J. Comput. Appl. Math., 2010

2006

Voronoi Random Fields.

[BibT_eX]

[DOI]