| Deep energy-based modeling of discrete-time physics T Matsubara, A Ishikawa, T Yaguchi Advances in Neural Information Processing Systems 33, 13100-13111, 2020 | 68 | 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 | 65 | 2021 |
| Preserving multiple first integrals by discrete gradients M Dahlby, B Owren, T Yaguchi Journal of Physics A: Mathematical and Theoretical 44 (30), 305205, 2011 | 62 | 2011 |
| 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 | 37 | 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 | 36 | 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 | 31 | 2012 |
| Conservative numerical schemes for the Ostrovsky equation T Yaguchi, T Matsuo, M Sugihara Journal of computational and Applied Mathematics 234 (4), 1036-1048, 2010 | 30 | 2010 |
| 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 | 23 | 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 | 23 | 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 | 18 | 2017 |
| A conservative compact finite difference scheme for the KdV equation H Kanazawa, T Matsuo, T Yaguchi JSIAM Letters 4, 5-8, 2012 | 17 | 2012 |
| FINDE: Neural differential equations for finding and preserving invariant quantities T Matsubara, T Yaguchi arXiv preprint arXiv:2210.00272, 2022 | 16 | 2022 |
| 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 35 (8), 10526-10538, 2023 | 15 | 2023 |
| 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 | 15 | 2018 |
| 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 | 10 | 2021 |
| Application of the variational principle to deriving energy-preserving schemes for the Hamilton equation A Ishikawa, T Yaguchi JSIAM Letters 8, 53-56, 2016 | 10 | 2016 |
| Kam theory meets statistical learning theory: Hamiltonian neural networks with non-zero training loss Y Chen, T Matsubara, T Yaguchi Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6322-6332, 2022 | 9 | 2022 |
| Deep discrete-time lagrangian mechanics T Aoshima, T Matsubara, T Yaguchi ICLR2021 Workshop on Deep Learning for Simulation (SimDL) 5, 2021 | 9 | 2021 |
| 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 | 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 | 7 | 2013 |
