2 citations · 8 across the 11 of their papers we have counts for
12 papers
Delay-coordinate reconstruction and conditional-moment causal diagnostics in stochastic systems
Jun Ohkubo
Partial observation and delay-coordinate reconstruction are rooted in deterministic dynamical-systems theory, whereas there are many systems with intrinsic stochasticity. We propos…
Extraction of linearized models from pre-trained networks via knowledge distillation
Fumito Kimura, Jun Ohkubo
Recent developments in hardware, such as photonic integrated circuits and optical devices, are driving demand for research on constructing machine learning architectures tailored f…
Koopman operator-based discussion on partial observation in stochastic systems
Jun Ohkubo
It is sometimes difficult to achieve a complete observation for a full set of observables, and partial observations are necessary. For deterministic systems, the Mori-Zwanzig forma…
Stochastic modeling of deterministic laser chaos using generator extended dynamic mode decomposition
Kakutaro Fukushi, Jun Ohkubo
Recently, chaotic phenomena in laser dynamics have attracted much attention to its applied aspects, and a synchronization phenomenon, leader-laggard relationship, in time-delay cou…
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…
Koopman analysis of combinatorial optimization problems with replica exchange Monte Carlo method
Tatsuya Naoi, Tatsuya Kishimoto, Jun Ohkubo
Combinatorial optimization problems play crucial roles in real-world applications, and many studies from a physics perspective have contributed to specialized hardware for high-spe…
Aspects of importance sampling in parameter selection for neural networks using ridgelet transform
Hikaru Homma, Jun Ohkubo
The choice of parameters in neural networks is crucial in the performance, and an oracle distribution derived from the ridgelet transform enables us to obtain suitable initial para…
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…
Compression of the Koopman matrix for nonlinear physical models via hierarchical clustering
Tomoya Nishikata, Jun Ohkubo
Machine learning methods allow the prediction of nonlinear dynamical systems from data alone. The Koopman operator is one of them, which enables us to employ linear analysis for no…
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,…
Redundant basis interpretation of Doi-Peliti method and an application
Shunta Takahashi, Jun Ohkubo
The Doi-Peliti method is effective for investigating classical stochastic processes, and it has wide applications, including field theoretic approaches. Furthermore, it is applicab…