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Yonekura Lab
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The lab aims for a future in which AI thinks and designs, using the power of mathematics and machine learning.
AboutAbout the lab
Realizing a future where AI “thinks and designs”
The Yonekura Lab pursues the fusion of mathematics and engineering, aiming to solve engineering problems with the power of mathematics.
Using machine learning such as generative models and reinforcement learning, the lab takes on the challenge of innovating the design of mechanical products.
The lab regards theory and application as the two wheels of its research, and places particular importance on industrial applications.
The lab holds lab seminars and other activities jointly with the Suzuki Lab.
Key terms
- Reinforcement learning
- A machine learning method that learns, through repeated trial and error, the actions that lead to good results.
- Topology optimization
- A design method that computationally finds the best-performing shape, including deciding where to place material and where to leave empty space.
- Lattice structure
- A form in which a fine lattice-like structure is repeated. It makes it possible to build lightweight structures.
NewsLab news
View More ↗ResearchResearch themes
The lab pursues the fusion of mathematics and engineering, aiming to solve problems across engineering fields with the power of mathematics. Its work includes data-driven design, which applies deep learning to mechanical design, as well as topology optimization and industrial applications of machine learning.
*Illustrative imageData-Driven Design
This research applies deep learning to mechanical design. The lab approaches it from three angles: predicting product performance, generating shapes that match a desired performance, and modifying shapes through reinforcement learning.
*Illustrative imageTopology Optimization
The lab developed the iSA method, which greatly shortens the computation time of topology optimization that takes fluid performance into account, and it has come to be used in design practice. The lab also studies the optimization of lattice structures made with 3D printers.
*Illustrative imageIndustrial Applications of Machine Learning
Aiming to support plant operation, the lab studies how to interpret what a machine learning model has learned. In collaboration with hospitals, it also conducts research that predicts the course of a patient's prognosis from medical images.
MembersFaculty
- Associate ProfessorKazuo YonekuraPublications ↗
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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: “Jet engine blades simulation” Dale Van Zante and Jay Horowitz / Public domain (cropped)
- Data-Driven Design: “VarVelA0T” OverG / CC BY 3.0 (cropped)
- Topology Optimization: “Fluid-Structure-Interaction-Topology-Optimization-1” Chrlundg / CC BY-SA 4.0 (cropped)
- Industrial Applications of Machine Learning: “Tuzla Thermal Power Plant02A” User:Felky / CC BY-SA 4.0 (cropped)
- Related lab (Aoyama Lab): “The production line” Jincong Peng / CC BY-SA 4.0 (cropped)
- Related lab (Izumi Lab): “Scramble from above, SHIBUYA SKY ( ) (cropped)” Sei F from Tokyo, Japan / CC BY-SA 2.0 (cropped)
- Related lab (Matsuo-Iwasawa Lab): “Deep-dream-white-noise-0036” MartinThoma / CC0 (cropped)
- Related lab (Ohsawa Lab): “Wikidata in the Linked Open Data cloud” Thomas Shafee / CC BY 4.0 (cropped)
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