Tutorial introduces SINDy for engineering equation recovery

By PulseAugur Editorial · [2 sources] · 2026-07-16 14:46

A new tutorial paper introduces the Sparse Identification of Nonlinear Dynamics (SINDy) method for engineering applications. SINDy addresses limitations of traditional surrogate modeling techniques like neural networks by recovering interpretable governing equations from smaller datasets. The paper details SINDy's extensions and provides case studies on identifying the system dynamics of an unmanned aerial vehicle and a chaotic thermosyphon heat exchanger. AI

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arXiv cs.LG TIER_1 English(EN) · Yao Cheng Li, Ana Larra\~naga, Steven L. Brunton, Urban Fasel · 2026-07-17 04:00

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. Surrogate modeling techniques such as neural networks can capture the behavior of these systems, but they typica…
arXiv cs.LG TIER_1 English(EN) · Urban Fasel · 2026-07-16 14:46

An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications

Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known. Surrogate modeling techniques such as neural networks can capture the behavior of these systems, but they typically demand large training datasets that are diff…