One-Shot Generation of Near-Optimal Topology through Theory-Driven Machine Learning

doi:doi:10.57702/h3vv27ca

One-Shot Generation of Near-Optimal Topology through Theory-Driven Machine Learning

The proposed learning mechanism uses a compliance minimization problem as a running example to learn a one-shot solution generator for topology optimization.

Data and Resources

Original MetadataJSON
The json representation of the dataset with its distributions based on DCAT.
Explore
- Preview
- Download

Cite this as

Ruijin Cang, Hope Yao, Yi Ren (2025). Dataset: One-Shot Generation of Near-Optimal Topology through Theory-Driven Machine Learning. https://doi.org/10.57702/h3vv27ca

DOI retrieved: January 2, 2025

Additional Info

Field	Value
Created	January 2, 2025
Last update	January 2, 2025
Defined In	https://doi.org/10.48550/arXiv.1807.10787
Author	Ruijin Cang
More Authors	Hope Yao Yi Ren