The data handled by machine learning is not necessarily aligned in a rectangular array like raster images. In this case, the data can be represented as graphs consisting of vertices and edges. However, capturing important features from graphs is a challenging problem. This talk discuss graph embedding, a technique used to extract features from graphs, and explore case studies that demostrate the usefulness of graph embedding in solving structural optimization problems, including topology optimization of trusses, sizing optimization of steel frames, and assembly sequence optimization.
How to join online
You can join online via Zoom, using the following link:
Referenten: Dr. Kazuki Hayashi , Assistant Professor, Department of Architecture and Architectural Engineering, Kyoto University, Japan
Zeit: 11:45 Uhr
Ort: Hybrid (Room 32-349 and via Zoom)