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Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM
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Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology

A recent study published on arXiv explores the use of sparse and nonparametric estimation methods to identify equations governing dynamical systems, with a particular focus on biological applications. The research demonstrates the effectiveness of sparse estimation techniques in learning mathematical models directly from complex data sets. This data-driven discovery approach addresses significant challenges faced by traditional modeling methods, which often struggle with the complexity and variability inherent in biological systems. By leveraging these advanced estimation methods, researchers can extract meaningful dynamical equations that describe system behavior more accurately. The study highlights the potential of these techniques to enhance understanding in fields where modeling dynamical processes is critical. Overall, the findings underscore the promise of data-driven approaches in advancing biological research through improved system identification.

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