Yao Cheng Li, Ana Larrañaga, Steven L. Brunton +1 more
Neural network surrogates can mimic a poorly understood system, but they want mountains of data and hand you a black box in return. SINDy takes the opposite bet. It assumes the true dynamics are a short sum of a few nonlinear terms, then uses sparse regression over a library of candidate terms to recover actual equations from comparatively little data.
This is a tutorial, not a new method. It starts with plain SINDy and works up through the practical variants: weak-form and ensembling tricks for noisy data, plus constrained and parametrizable formulations. Two case studies carry the point, identifying the dynamics of an unmanned aerial vehicle and of a chaotic thermosyphon heat exchanger, with code on GitHub.
The abstract only gestures at the details, so the paper and its accompanying repository are where the real work sits.
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 difficult to obtain in engineering contexts and yield models with limited physical interpretability. The Sparse Identification of Nonlinear Dynamics (SINDy) method addresses both limitations by…
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