Stream-Based Active Learning with Cooperative Neural Networks for Data-Efficient Partial Inverse Design: An Automotive Glass Run Channel Case Study
Read the original on arXiv Machine Learning →The paper introduces CoNN-AL, a framework that combines stream-based active learning with a Cooperative Neural Network and Denoising Autoencoder to efficiently perform partial inverse design in engineering. By estimating predictive uncertainty with Monte Carlo dropout, the method selectively labels the most informative samples in real time, dramatically reducing the number of required simulations. Applied to an automotive glass run channel dataset of over 900,000 designs, CoNN-AL achieves R-squared values between 0.967 and 0.982 using only 2.3% of the full training set, outperforming random sampling and approaching the performance of models trained on much larger datasets.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.