Hugging Face Trending Papers June 10, 2026

How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

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Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches relied on empirical data and heuristics, modern data-driven methods offer greater flexibility and fewer assumptions.

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arXiv Machine Learning

Jun 11

How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

arXiv:2606. 12182v1 Announce Type: new Abstract: Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering.

By Ana Larra\~naga, Urban Fasel, Steven L. Brunton

arXiv Machine Learning

Jul 17

An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications

arXiv:2607. 15077v1 Announce Type: new Abstract: Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known.

By Yao Cheng Li, Ana Larra\~naga, Steven L. Brunton, Urban Fasel

benchmarks safety

arXiv Machine Learning

Jun 16

Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements

arXiv:2606. 15690v1 Announce Type: new Abstract: Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity.

By Filippo Zacchei, Ana Larra\~naga, Attilio Frangi, Andrea Manzoni, Steven L. Brunton

benchmarks

arXiv Machine Learning

Jul 28

Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification

arXiv:2604. 20141v2 Announce Type: replace Abstract: We introduce Fourier Weak SINDy, a minimal noise-robust and interpretable derivative-free equation learning method that combines weak-form sparse equation learning with spectral density estimation for data-driven test function selection.

By Zhiheng Chen, Urban Fasel, Anastasia Bizyaeva

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arXiv Machine Learning

Jun 5

PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability

arXiv:2606. 05191v1 Announce Type: new Abstract: Data-driven equation discovery is fundamentally an inverse problem that seeks to infer the governing differential equations of a system directly from time-series measurements.

By Federico J. Gonzalez

arXiv Machine Learning

Aug 3

Dynamics-aware identification of governing equations from sparse and noisy data

arXiv:2607. 29036v1 Announce Type: new Abstract: Sparse identification of nonlinear dynamics (SINDy) and PDE functional identification (PDE-FIND) recover parsimonious ordinary and partial differential equations (ODEs and PDEs) from data.

By Pongpisit Thanasutives, Yoshinobu Kawahara

diffusion benchmarks

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How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications

Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements

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Dynamics-aware identification of governing equations from sparse and noisy data