Stream-Based Active Learning with Cooperative Neural Networks for Data-Efficient Partial Inverse Design: An Automotive Glass Run Channel Case Study
We propose CoNN-AL, a framework for data-efficient partial inverse design that adds stream-based active learning to the Cooperative Neural Network with Denoising Autoencoder (CoNN-DAE).
ProofPaper ↗
Key points
- Inverse design in engineering often runs into a simple problem.
- This study addresses that problem for partial inverse design, where only some design variables are specified and the rest must be inferred to reach a target performance value.
- We validate the framework on a real-world automotive glass run channel dataset of more than 900,000 unique simulated designs.
- Together with this work, we publicly release the dataset to support future research on data-driven design.
Sources (1)
- [1]Stream-Based Active Learning with Cooperative Neural Networks for Data-Efficient Partial Inverse Design: An Automotive Glass Run Channel Case StudyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 11:07 AM
We propose CoNN-AL, a framework for data-efficient partial inverse design that adds stream-based active learning to the Cooperative Neural Network with Denoising Autoencoder (CoNN-DAE).
Inverse design in engineering often runs into a simple problem.
Extractive summary: sentences quoted from the sources.