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September 10, 2025Proceedings of the Royal Society A Mathematical Physical and Engineering SciencesOpen Access

BINDy: Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte Carlo

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Authors

MCMax D. ChampneysIR Dynamics (United States)TRTimothy J. RogersUniversity of Sheffield

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Overview

Proposed method improves sparse identification of nonlinear dynamics in data, suggesting enhanced model parsimony.

Key Points

  • BINDy outperforms ensemble SINDy in identifying correct model terms in three benchmark case-studies.
  • Bayesian approach allows for flexible modeling using arbitrary priors over the model structure.
  • Reversible-jump Markov-chain Monte Carlo enables inference over parameter vectors of varying dimensions.
  • The method emphasizes model parsimony, reducing the risk of over-fitting and enhancing interpretability.

Cite This Study

Champneys et al. (2025) studied this question.

synapsesocial.com/papers/68c1bb6354b1d3bfb60ecfcchttps://doi.org/10.1098/rspa.2024.0620
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Also Consider

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  1. 1Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data2024 · 5 citations
  2. 2SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study2024
  3. 3Data-driven system identification of unknown systems utilising sparse identification of nonlinear dynamics (SINDy)2024 · 3 citations
  4. 4Iterative Sparse Identification of Nonlinear Dynamics2024
  5. 5Multi-objective SINDy for parameterized model discovery from single transient trajectory data2024