New framework improves data efficiency in engineering inverse design

Researchers have developed a new framework called CoNN-AL to address the high cost of data acquisition in engineering inverse design problems. This framework integrates stream-based active learning with a Cooperative Neural Network with Denoising Autoencoder (CoNN-DAE) to efficiently select the most informative samples for labeling. Tested on a large automotive glass run channel dataset, CoNN-AL achieved high R-squared values using significantly fewer labels than random sampling, demonstrating its effectiveness in data-efficient design. AI

IMPACT This framework could significantly reduce the cost and time required for data-intensive engineering design processes.

RANK_REASON Academic paper detailing a new machine learning framework and its application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Automotive Glass Run Channel
• CoNN-DAE
• Cooperative Neural Network with Denoising Autoencoder
• Monte Carlo dropout

• paper
• other

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