Takaharu YAGUCHI

Takaharu YAGUCHI

Department of Mathematics,
Graduate School of Science, Kobe University
Division : Applied Mathematics
Professor		 Building B, Room 322

Research Field : Development and theoretical analysis of information technologies, particularly physical simulation and deep learning
Research Summary : I am addressing various real world problems through a mathematical approach. Currently, I am focusing on the development of methods of deep geometric scientific computation, in which deep learning, geometric mechanics and physical simulation techniques are combined.
Primary Publications :
  1. T. Matsubara and T. Yaguchi, FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities, Proc. of The Eleventh International Conference on Learning Representations (ICLR2023), Kigali, May 2023.
  2. Y. Chen, T. Matsubara, and T. Yaguchi, KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss, Proc. of The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022, oral), Virtual, Feb. 2022.
  3. Y. Chen, T. Matsubara, and T. Yaguchi, Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems, Advances in Neural Information Processing Systems 34 (NeurIPS2021, spotlight), Virtual, Dec. 2021.
  4. T. Matsubara, A. Ishikawa, T. Yaguchi, Deep Energy-based Modeling of Discrete-Time Physics, Advances in Neural Information Processing Systems (NeurIPS, oral), Virtual, Dec. 2020.
  5. M. Komatsu, T. Yaguchi, K. Nakajima, Algebraic approach towards the exploitation of "softness": the input-output equation for morphological computation, International Journal of Robotics Research, 40 (2021) 99-118
  6. K. Masumoto, T. Yaguchi, H. Matsuda, H. Tani, K. Totsuka, N. Kondo and S. Okada, Measurement and visualization of face-to-face interaction among community-dwelling elderly persons using wearable sensors, Geriatrics and Gerontology International, 17 (2017) 1752--1758.
  7. T. Yaguchi, A Lagrangian Approach to Deriving Energy-Preserving Numerical Schemes for the Euler-Lagrange Partial Differential Equations, M2AN, 47 (2013) 1493-1513.
  8. T. Yaguchi, T. Matsuo and M. Sugihara, "The Discrete Variational Derivative Method Based on Discrete Differential Forms," J. Comput. Phys., 231 (2012) 3963-3986.