Midhun-Kanadan/Machine-Learning-Models-for-Topology-Optimization
This project explores the integration of Machine Learning (ML) and Deep Learning (DL) techniques in the field of topology optimization. Leveraging the TOP dataset, the study presents a comprehensive approach to optimizing structural designs using advanced neural network architectures.
This project helps structural engineers and product designers quickly find optimal designs for structures. You input design scenarios, and it outputs efficient, optimized structural layouts. This is for professionals who need to rapidly iterate on structural designs, reducing manual trial and error.
No commits in the last 6 months.
Use this if you are a structural engineer or designer needing to quickly generate optimized structural forms for various load and boundary conditions.
Not ideal if you require highly specialized, custom-coded optimization algorithms, or if your design problems are outside the scope of typical 2D grid-based topology optimization.
Stars
17
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 16, 2024
Commits (30d)
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