6 papers
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…