2 citations · 5 across the 4 of their papers we have counts for
4 papers
Integrated utilization of equations and small dataset in the Koopman operator: applications to forward and inverse problems
Ichiro Ohta, Shota Koyanagi, Kayo Kinjo +1
In recent years, there has been a growing interest in data-driven approaches in physics, such as extended dynamic mode decomposition (EDMD). The EDMD algorithm focuses on nonlinear…
Permutation of Tensor-Train Cores for Computing Moments on Stochastic Differential Equations
Kayo Kinjo, Rihito Sakurai, Tatsuya Kishimoto +1
Tensor networks, particularly the tensor train (TT) format, have emerged as powerful tools for high-dimensional computations in physics and computer science. In solving coupled dif…
Improvement of system identification of stochastic systems via Koopman generator and locally weighted expectation
Yuki Tahara, Kakutaro Fukushi, Shunta Takahashi +2
The estimation of equations from data is of interest in physics. One of the famous methods is the sparse identification of nonlinear dynamics (SINDy), which utilizes sparse estimat…
Extraction of nonlinearity in neural networks with Koopman operator
Naoki Sugishita, Kayo Kinjo, Jun Ohkubo
Nonlinearity plays a crucial role in deep neural networks. In this paper, we investigate the degree to which the nonlinearity of the neural network is essential. For this purpose,…