教員一覧 List of faculty members
YOSHINAGA Natsuhiko 教授
YOSHINAGA, Natsuhiko Professor
Natsuhiko Yoshinaga Professor
Natsuhiko YoshinagaBelongs:
Department of Complex and Intelligent Systems; Complex Systems Information Science
Field of Study
Nonlinear dynamics of soft materials, mathematical models of cellular organisms, and model estimation using machine learning.Previous employment/history
Associate Professor, Mathematical Collaboration Group, Advanced Institute for Materials Research (WPI-AIMR), Tohoku University; Chief Researcher and Deputy Lab Director, Open Innovation Laboratory for Advanced Mathematical Materials, National Institute of Advanced Industrial Science and Technology (AIST) and Tohoku University (Cross Appointment)Subjects in charge (undergraduate)
Mathematical Modeling, Fundamentals of Data Science, Machine Learning I, Formal Languages and AutomataSubjects taught (Graduate School)
Advanced Topics in Information MathematicsBachelor of Science
Doctor of Science, Kyoto University
YOSHINAGA, Natsuhiko Professor
Affiliation：
Department of Complex and Intelligent Systems
Research Fields
Nonlinear dynamics of soft materials, theoretical modelling of biological cells, governing equation discovery by machine learningAcademic Background
Associate Professor, Mathematical Science Group, WPI Advanced Institute for Materials Research (WPI-AIMR), Tohoku UniversitySubjects in Charge (Undergraduate)
Mathematical Modelling, Basics of Data Science, Machine Learning I, Formal Languages and AutomataSubjects in Charge (Graduate School)
Advanced Topics in Information MathematicsDegree
PhD in Science, Kyoto UniversityRelated Links
Related Links
Research Projects
My research theoretically investigates how ordered structures (patterns) appear in natural phenomena. In particular, I mathematically analyze how soft materials, such as polymers like jelly and liquid crystals used in televisions, as well as biological cells, create and break down beautiful structures. These phenomena are complex, but I am conducting research to estimate mathematical models using machine learning.
The appeal of research
The natural world is full of mysterious phenomena. Even understanding just one of them can lead to new insights that no one has ever known before. Natural phenomena may seem complex at first glance, but sometimes they can be understood in a very simple way. By understanding one thing mathematically, we begin to realize that things that were previously considered separate can be understood in the same way. I believe these experiences can only be gained through research.
Achievements
We proposed a machine learning method to estimate the governing equations for realizing the desired structure in the self-assembly phenomena of polymers and colloids. Furthermore, we have constructed and analyzed nonlinear waves of protein concentrations involved in cell division, mathematical models of spontaneously moving droplets, and mathematical models of periodic structures observed in the cytoskeleton.
Major publications and papers
- Natsuhiko Yoshinaga and Satoru Tokuda, “Bayesian Modeling of Pattern Formation from One Snapshot of Pattern”, Physical Review E, 106, 065301 (2022)
- Uyen Tu Lieu and Natsuhiko Yoshinaga, “Inverse design of two-dimensional structure by self-assembly of patchy particles”, Journal of Chemical Physics, 156, 054901 (2022)
- Shunshi Kohyama, Natsuhiko Yoshinaga, Miho Yanagisawa, Kei Fujiwara, Nobuhide Doi, “Cell-sized confinement controls generation and stability of a protein wave for spatiotemporal regulation in cells”, eLife, 8, e44591 (2019)
- Natsuhiko Yoshinaga, Tanniemola B. Liverpool, “Hydrodynamic interactions in dense active suspensions:from polar order to dynamical clusters”, Physical Review E Rapid Communications, 96, 020603(R) (2017)
- Natsuhiko Yoshinaga, “Spontaneous motion and deformation of a self-propelled droplet”, Physical Review E, 89, 012913 (2014)
- Natsuhiko Yoshinaga, Jean-Francois Joanny, Jacques Prost and Pilippe Marcq, “Polarity patterns of stress fibers”, Physical Review Letters, 105, 238103 (2010)
Research Contents
Our group is theoretically studying the dynamics of pattern formation. A particular focus is on soft materials, such as polymers in gels, liquid crystals used TV, and biological cells. Those systems are very complex, but with the aid of machine learning, we may estimate mathematical models to understand them.
Attractive Factors of My Research
Nature is full of interesting phenomena. Understanding a small piece of it gives us new insight which nobody has done. A natural phenomonon is very complex, but still, it is often understood by simple theoretical idea. Mathematical science would help us find common aspects of completely different phenomena. Research would lead you to these experiences.
Achievement
Our group has proposed a machine-learning approach to estimate governing equations for the pattern formation of polymers and colloidal particles. Our research incudes nonlinear waves of proteins which play an essential role on cell division, modelling of self-propelled drops, and theoretical models for periodic patterns in cytoskelton.
Major Books and Papers
- Natsuhiko Yoshinaga and Satoru Tokuda, “Bayesian Modelling of Pattern Formation from One Snapshot of Pattern”, Physical Review E, 106, 065301 (2022)
- Uyen Tu Lieu and Natsuhiko Yoshinaga, “Inverse design of two-dimensional structure by self-assembly of patchy particles”, Journal of Chemical Physics, 156, 054901 (2022)
- Shunshi Kohyama, Natsuhiko Yoshinaga, Miho Yanagisawa, Kei Fujiwara, Nobuhide Doi, “Cell-sized confinement controls generation and stability of a protein wave for spatiotemporal regulation in cells”, eLife, 8, e44591 (2019)
- Natsuhiko Yoshinaga, Tanniemola B. Liverpool, “Hydrodynamic interactions in dense active suspensions:from polar order to dynamical clusters”, Physical Review E Rapid Communications, 96, 020603(R) (2017)
- Natsuhiko Yoshinaga, “Spontaneous motion and deformation of a self-propelled droplet”, Physical Review E, 89, 012913 (2014)
- Natsuhiko Yoshinaga, Jean-Francois Joanny, Jacques Prost and Pilippe Marcq, “Polarity patterns of stress fibers”, Physical Review Letters, 105, 238103 (2010)
Research seeds related to this faculty member
Discover mathematical models from data.
field of study:
Mathematical Modeling Data science Nonlinear systemskeyword:
# materials science # Self-organizationNEWS
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