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Interdisciplinary Fusion Lab
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The lab studies the atmosphere and semiconductors using data assimilation and machine learning.
AboutAbout the lab
Exploring the atmosphere and semiconductors through data assimilation and machine learning
The Interdisciplinary Fusion Lab is a lab at the Research Institute for Applied Mechanics. Its head is Associate Professor Makoto Hasegawa of the Fusion Dynamics Division.
The other faculty members are Professor Keiya Yumimoto of the Earth Environmental Dynamics Division, and Associate Professor Akira Kusaba and Assistant Professor Hidekazu Tsuji of the New Energy Dynamics Division.
All faculty members also hold concurrent appointments in the Department of Interdisciplinary Engineering Sciences of the Interdisciplinary Graduate School of Engineering Sciences.
Key terms
- Data assimilation
- A method that incorporates observational data into numerical models to improve the accuracy of predictions and estimates
- Aerosol
- Tiny particles suspended in the atmosphere, such as Asian dust and PM2.5
- Bayesian optimization
- A method that selects the next candidate based on the results of previous trials, searching for good conditions with few trials
ResearchResearch themes
Professor Keiya Yumimoto studies atmospheric aerosols, and Associate Professor Akira Kusaba studies semiconductor crystal growth. In their respective research, Prof. Yumimoto uses data assimilation, while Associate Professor Kusaba uses machine learning and data assimilation.
*Illustrative imageData Assimilation Prediction of Aerosols
Professor Keiya Yumimoto studies atmospheric aerosols such as Asian dust, PM2.5, and forest fire smoke by combining numerical models with satellite and ground-based observations. The aim is to develop methods for analyzing and predicting changes in the atmospheric environment more accurately.
*Illustrative imageMachine Learning for Semiconductor Crystal Growth
Associate Professor Akira Kusaba applies machine learning to the crystal growth of next-generation semiconductors. Research topics include searching for surface structures using Bayesian optimization and studying reaction rate constants using data assimilation.
MembersFaculty
- Associate ProfessorMakoto Hasegawa
- ProfessorKeiya Yumimoto
- Associate ProfessorAkira Kusaba
- Assistant ProfessorHidekazu Tsuji
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: “Aerosol 1” Sciencia58 / CC0 (cropped)
- Data Assimilation Prediction of Aerosols: “BCC AQI Board” Arijit Kisku / CC BY 4.0 (cropped)
- Machine Learning for Semiconductor Crystal Growth: “Silicon single crystal” Massimiliano Lincetto / CC BY-SA 4.0 (cropped)
- Related lab (Plasma Information Control Lab): “Electric glow discharge schematic-fr” Electric_glow_discharge_schematic.png: http://en.wikipedia.org/wiki/User:Iantresman derivative work: Grimlock (talk) / CC BY 2.5 (cropped)
- Related lab (Theoretical Plasma Physics Lab): “Pilot Plasma Engine - GPN” NASA / Public domain (cropped)
- Related lab (Fusion Simulation Lab): “U.S. Department of Energy - Science - )” U.S. Department of Energy from United States / Public domain (cropped)
- Related lab (Beam Plasma Diagnostics Lab): “A segment of the Wendelstein 7-X vacuum vessel” Siarhei Besarab / CC BY-SA 4.0 (cropped)
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