An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
Pool-based sequential active learning for regression.IEEE Transactions on Neural Networks and Learning Systems, 30(5):1348–1359, 2019
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Active learning for optimal experimental design in ML-based building energy system identification yields up to 54% lower RMSE than passive random sampling on the BOPTEST simulator across neural network and Gaussian process models.
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How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.