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.
arXiv preprint arXiv:2507.11739 , year=
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Fourier Weak SINDy learns ODE coefficients from noisy time series by regressing Fourier coefficients that are selected via multitaper spectral density estimation, beating SINDy baselines on chaotic benchmarks.
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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.
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Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification
Fourier Weak SINDy learns ODE coefficients from noisy time series by regressing Fourier coefficients that are selected via multitaper spectral density estimation, beating SINDy baselines on chaotic benchmarks.