Professor, Department of Mathematics, Graduate School of Science, Kobe UniversityJST CREST "Structure-Preserving System Modeling and Simulation Basis Based on Geometric Discrete Mechanics"
1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501
I am looking for Ph.D. students with research interest in deep learning for physical simulation and modeling.
I am addressing various real world problems through a mathematical scientific approach. In particular, I am conducting research on simulation and modeling from both theoretical and practical aspects.
With the recent development of sensor devices, it has become possible to acquire a variety of data. I am particularly interested in statistical methods for data obtained as networks in psychology, agriculture and economics.
In recent years, it has been recognized that a variety of functionalities can be realized only by the movement of the body, as typified by the passive walk of a robot. I am studying these problems from modeling and simulation approaches.
I am conducting research to simulate the sound of a musical instrument by physical simulations of the movement of piano strings, hammers and other parts of the instrument, thereby developing novel physics-based electronic instruments.
In order to develop a computer-based physics simulation method, I am working on the physics of the digital world.
I am studying input-output relations using differential algebra for the estimation and analysis of model parameters for simulation.
Traditionally, the target of time-series analysis has been numerical data. I am developing a time-series analysis method for non-numerical time-evolution data (e.g. evolutional networks) using the information geometry.
With the recent development of machine learning techniques such as deep learning, researches to integrate such techniques with scientific computing are in progress. Such integration will enable us to perform scientific computations based on models that are more compatible with data than ever before, and to perform parallel computing using GPUs, which are widely used in the field of deep learning.
Based on the above, we are developing a modeling and simulation platform that enables us
Y. Chen, T. Matsubara, and T. Yaguchi, “KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss,” AAAI, 2022. (Oral, acceptance rate 4.3%)
T. Matsubara, Y. Miyatake, and T. Yaguchi, “Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory,” NeurIPS, 2021. (Poster, acceptance rate 26%)
Y. Chen, T. Matsubara, and T. Yaguchi, “Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems,” NeurIPS, 2021. (Spotlight, acceptance rate 3%)
Shunpei Terakawa, Takashi Matsubara, Takaharu Yaguchi, The Error Analysis of Numerical Integrators for Deep Neural Network Modeling of Differential Equations, the Machine Learning and the Physical Sciences (Workshop at NeurIPS 2020.)
Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi, Deep Energy-based Modeling of Discrete-Time Physics, Advances in Neural Information Processing Systems (NeurIPS), 2020.
M. Komatsu, S. Terakawa and T. Yaguchi, Energetic-Property-Preserving Numerical Schemes for Coupled Natural Systems, Mathematics.
T. Satoh and T. Yaguchi, On the equivalence of the norms of the discrete diffrential forms in discrete exterior calculus, Japan Journal of Industrial and Applied Mathematics.