Preserving multiple first integrals by discrete gradients M Dahlby, B Owren, T Yaguchi Journal of Physics A: Mathematical and Theoretical 44 (30), 305205, 2011 | 54 | 2011 |

Deep energy-based modeling of discrete-time physics T Matsubara, A Ishikawa, T Yaguchi Advances in Neural Information Processing Systems 33, 13100-13111, 2020 | 49 | 2020 |

Neural symplectic form: Learning Hamiltonian equations on general coordinate systems Y Chen, T Matsubara, T Yaguchi Advances in Neural Information Processing Systems 34, 16659-16670, 2021 | 34 | 2021 |

An extension of the discrete variational method to nonuniform grids T Yaguchi, T Matsuo, M Sugihara Journal of Computational Physics 229 (11), 4382-4423, 2010 | 34 | 2010 |

Conservative numerical schemes for the Ostrovsky equation T Yaguchi, T Matsuo, M Sugihara Journal of computational and Applied Mathematics 234 (4), 1036-1048, 2010 | 27 | 2010 |

The discrete variational derivative method based on discrete differential forms T Yaguchi, T Matsuo, M Sugihara Journal of Computational Physics 231 (10), 3963-3986, 2012 | 26 | 2012 |

Symplectic adjoint method for exact gradient of neural ode with minimal memory T Matsubara, Y Miyatake, T Yaguchi Advances in Neural Information Processing Systems 34, 20772-20784, 2021 | 22 | 2021 |

Numerical integration of the Ostrovsky equation based on its geometric structures Y Miyatake, T Yaguchi, T Matsuo Journal of Computational Physics 231 (14), 4542-4559, 2012 | 22 | 2012 |

Measurement and visualization of face‐to‐face interaction among community‐dwelling older adults using wearable sensors K Masumoto, T Yaguchi, H Matsuda, H Tani, K Tozuka, N Kondo, S Okada Geriatrics & gerontology international 17 (10), 1752-1758, 2017 | 15 | 2017 |

A conservative compact finite difference scheme for the KdV equation H Kanazawa, T Matsuo, T Yaguchi JSIAM Letters 4, 5-8, 2012 | 15 | 2012 |

Algebraic approach towards the exploitation of “softness”: The input–output equation for morphological computation M Komatsu, T Yaguchi, K Nakajima The International Journal of Robotics Research 40 (1), 99-118, 2021 | 12 | 2021 |

Mass-spring damper array as a mechanical medium for computation Y Yamanaka, T Yaguchi, K Nakajima, H Hauser International Conference on Artificial Neural Networks, 781-794, 2018 | 9 | 2018 |

Application of the variational principle to deriving energy-preserving schemes for the Hamilton equation A Ishikawa, T Yaguchi JSIAM Letters 8, 53-56, 2016 | 9 | 2016 |

Finde: Neural differential equations for finding and preserving invariant quantities T Matsubara, T Yaguchi arXiv preprint arXiv:2210.00272, 2022 | 8 | 2022 |

Secret communication systems using chaotic wave equations with neural network boundary conditions Y Chen, H Sano, M Wakaiki, T Yaguchi Entropy 23 (7), 904, 2021 | 8 | 2021 |

Deep discrete-time Lagrangian mechanics T Aoshima, T Matsubara, T Yaguchi ICLR2021 Workshop on Deep Learning for Simulation (SimDL) 5, 2021 | 8 | 2021 |

Lagrangian approach to deriving energy-preserving numerical schemes for the Euler–Lagrange partial differential equations∗ T Yaguchi ESAIM: Mathematical Modelling and Numerical Analysis 47 (5), 1493-1513, 2013 | 6 | 2013 |

Secure communication systems using distributed parameter chaotic synchronization H Sano, M Wakaiki, T Yaguchi Transactions of the Society of Instrument and Control Engineers 57 (2), 78-85, 2021 | 5 | 2021 |

Geometric investigation of the discrete gradient method for the Webster equation with a weighted inner product A Ishikawa, T Yaguchi JSIAM Letters 7, 17-20, 2015 | 5 | 2015 |

The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations T Matsubara, Y Miyatake, T Yaguchi IEEE Transactions on Neural Networks and Learning Systems, 2023 | 4 | 2023 |