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Inubushi Lab
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The lab explores nonlinear phenomena in turbulence and machine learning using dynamical systems theory.
AboutAbout the lab
Understanding nonlinear phenomena through dynamical systems theory
The Inubushi Lab uses dynamical systems theory, a branch of mathematics, to study nonlinear problems that arise in fluid motion and machine learning.
Dynamical systems theory is a field of mathematics that investigates mathematical models such as differential equations. In nonlinear models, a seemingly random phenomenon called chaos can occur even without any stochastic element.
The lab applies this theory to research aimed at understanding, predicting, and controlling nonlinear phenomena.
Recently, it has also been working on applying methods from information science to problems in fluid dynamics.
Key terms
- Dynamical systems theory
- A branch of mathematics that studies mathematical models such as differential equations
- Chaos
- A seemingly random phenomenon that occurs in nonlinear mathematical models even without any stochastic element
- Reservoir computing
- A machine learning method that approximates the nonlinear relationship between input and output time series using a model that is linear in its trainable parameters
- Reinforcement learning
- A machine learning method suited to optimization problems over an entire time horizon
NewsLab news
A paper on data assimilation of two-dimensional turbulence was published in the Journal of Fluid Mechanics and was also selected for the cover
A paper co-authored with a master's student was published in Scientific Reports
Associate Professor Inubushi received the Ryumon Award from the Japan Society of Fluid Mechanics
A paper on data assimilation of turbulence was published in Physical Review Letters
ResearchResearch themes
Interested in mathematics, and in particular in applications of dynamical systems theory, the lab mainly studies nonlinear problems that arise in fluid dynamics (stability, turbulence, mixing) and information science (machine learning, data assimilation, random number generation). Recently, it has also been working on applying methods from information science to problems in fluid dynamics.
*Illustrative imageInvestigating fluid motion with dynamical systems
The lab investigates the motion of fluids governed by the Navier–Stokes equations from the viewpoint of dynamical systems theory. It studies two-dimensional Kolmogorov flow, three-dimensional Couette flow, and turbulence in a periodic box, mainly through Lyapunov analysis.
*Illustrative imageMathematics of reservoir computing
Reservoir computing is a machine learning method that approximates the nonlinear relationship between input and output time series using a model that is linear in its trainable parameters. The lab investigates its mathematical properties from the viewpoint of dynamical systems theory, and has also proposed a transfer learning method that can learn from small amounts of data.
*Illustrative imageOptimizing fluid mixing with reinforcement learning
Obtaining an efficient mixing method requires solving an optimization problem over the entire time horizon, so the lab applies reinforcement learning, a machine learning method suited to such problems, to the optimization of fluid mixing. For the mixing problem of a concentration field governed by the advection–diffusion equation, it has shown that deep reinforcement learning is effective.
MembersFaculty
- Associate ProfessorMasanobu Inubushi
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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: “Turbulence arbre 2 modifié-1” Lamiot / CC BY-SA 3.0 (cropped)
- Investigating fluid motion with dynamical systems: “Airplane vortex edit” NASA Langley Research Center (NASA-LaRC), Edited by Fir0002 / Public domain (cropped)
- Mathematics of reservoir computing: “Artificial Neural Network with Chip” mikemacmarketing / original posted on flickr Liam Huang / clipped and posted on flickr / CC BY 2.0 (cropped)
- Optimizing fluid mixing with reinforcement learning: “CSIRO ScienceImage 2797 Textile Dye in Water” Textile and Fibre Technology, CSIRO / CC BY 3.0 (cropped)
- Related lab (中山研究室): “Răstoliţa, train schedule” cs:ŠJů / CC BY-SA 3.0 (cropped)
- Related lab (橋口研究室): “Random-pixels-black-white” Georg-Johann / CC0 (cropped)
- Related lab (石渡研究室): “Star Cluster R )” Credit: NASA, ESA, and STScI / Public domain (cropped)
- Related lab (関川研究室): “Abacus hg” Hannes Grobe / CC BY-SA 4.0 (cropped)
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