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Nonlinear Physics Lab
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A lab that uses knowledge of mathematics, physics, and informatics to solve nonlinear phenomena and data theoretically.
AboutAbout the lab
Taking on nonlinear phenomena with mathematics, physics, and informatics
The Nonlinear Physics Lab uses knowledge from mathematics, physics, and informatics to carry out a wide range of theoretical research across disciplines.
The lab works on analysis of nonlinear science and complex systems such as chaos and fractals, mathematical modeling, analysis of machine learning and brain-inspired AI based on nonlinear dynamics, and analysis of real-world data such as medical data.
The lab says that students do not need to have all the necessary knowledge when they enter; they can learn what they need in meetings, lab seminars, and reading groups.
Undergraduate students are assigned to the lab in their fourth year from the School of Engineering's Department of Interdisciplinary Engineering (Mechanical and Electrical Engineering course), and the lab also accepts students from the KOSEN cooperative education program.
Key terms
- Dynamical robustness
- The degree to which a dynamical system can keep functioning when it is damaged
- Coupled oscillator system
- A system in which multiple oscillators interact and influence one another's states. Phenomena such as synchronization appear in it
- Reservoir computing
- A type of recurrent neural network that uses random connections, so the computational cost of learning is low
ResearchResearch themes
The lab uses knowledge from mathematics, physics, and informatics to carry out a wide range of theoretical research across disciplines. The lab's page gives the dynamical robustness of systems, analysis of medical data, machine learning, and the development of mathematical models as concrete examples.
*Illustrative imageCoupled oscillator systems and dynamical robustness
The lab studies whether a system of coupled oscillators can keep oscillating after it is damaged. It extends the problem to various networks, and also covers efficient ways to recover after damage and ways to produce oscillation from combinations of elements that cannot oscillate on their own.
*Illustrative imagePrediction algorithms for medical data
Using medical data obtained in clinical settings and elsewhere, the lab has built algorithms that predict prognosis for conditions such as prostate cancer, glaucoma, and chronic myeloid leukemia, drawing on machine learning and dynamical systems. Development is also continuing with new medical data.
*Illustrative imageMachine learning using nonlinear dynamics
Aiming for algorithms with high learning accuracy and efficiency, the lab develops methods that incorporate knowledge of nonlinear dynamics. With collaborators inside and outside the university, it also applies reservoir computing to predicting quantities such as the engine output of space rockets, and works to understand the mechanisms of learning.
*Illustrative imageDeveloping mathematical models for real-world problems
The lab develops mathematical models that describe natural phenomena and engineering and social problems with equations. It works on various applied problems with collaborators inside and outside the university, but details are not disclosed because the research is still in progress.
MembersFaculty
The members of the Nonlinear Physics Lab consist of faculty, secretaries and staff, and master's and undergraduate students.As of 2026
- ProfessorKai Morino
- Assistant ProfessorTomokatsu Onaga
- Assistant ProfessorMiki Kaneshige
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “TFF Double pendulum” Jedidiah T-F / CC0 (cropped)
- Coupled oscillator systems and dynamical robustness: “Cycle synchronization time 0” RichiRan / CC BY-SA 3.0 (cropped)
- Prediction algorithms for medical data: “Medico de un hospital de la pandemia Covid-19” Pablo Jarrín / CC BY-SA 4.0 (cropped)
- Machine learning using nonlinear dynamics: “Chaos (tarot Etteilla III)” Guilaume Urban Régamey (1837–1875) / Public domain (cropped)
- Developing mathematical models for real-world problems: “Chaos Theory & Double Pendulum - 4” Cristian V. / CC BY-SA 4.0 (cropped)
- Related lab (Nonequilibrium Plasma Dynamics Lab): “IPT-plasma-jet” Endeavour88 / CC BY-SA 4.0 (cropped)
- Related lab (Nanomaterials and Device Science Lab): “Transmission electron microscope (Morgagni 268D) 4pl” Pleple2000 / CC BY-SA 3.0 (cropped)
- Related lab (Energy and Thermophysical Science Lab): “Close-up of a Fire” Thomas Kirchel / CC BY 4.0 (cropped)
- Related lab (Biological Energy Engineering Lab): “Yeast cultures in incubator-2” Masur / CC BY-SA 4.0 (cropped)
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