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December 22, 2025Open Access

Information theory and discriminative sampling for model discovery

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Authors

YBYuxuan BaoCivil Aviation Flight University of ChinaJKJ. Nathan KutzUniversity of Washington

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Implication

This research demonstrates the impact of information theory on model performance and sampling strategies in dynamical systems, indicating improved learning efficiency.

Key Points

  • This work explores how Fisher information and Shannon entropy aid in model discovery and data efficiency in dynamical systems.
  • Leveraging Fisher Information Matrix within sparse identification of nonlinear dynamics (SINDy) framework.
  • Visualizing information patterns in chaotic and non-chaotic systems.
  • Conducting spectral analysis to elucidate benefits of statistical bagging.
  • Demonstrated improved sampling efficiency by prioritizing informative data.
  • Showed enhanced model performance with information-based analysis.
  • Highlighted the role of Fisher information in achieving data efficiency in various scenarios.

Cite This Study

Bao et al. (2025) studied this question.

synapsesocial.com/papers/69488bc877063b71e748ce73https://doi.org/10.48550/arxiv.2512.16000
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  1. 1Dynamic Importance Learning using Fisher Information Matrix (FIM) for Nonlinear Dynamic Mapping2024
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  5. 5Data-driven system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy)2024 · 3 citations