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Bayesian Tree-Adjoining Grammars Enhance Equation Discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a Bayesian approach to equation discovery using Tree-Adjoining Grammars (TAGs). This method defines a generative prior over tree structures and parameters, employing a Reversible-Jump MCMC sampler to infer the joint posterior distribution. The approach was tested on simulated polynomial NARX systems, the Silverbox benchmark, and wave-loading data, demonstrating its capability in quantifying uncertainty and fitting physics-informed models for dynamical systems. AI

IMPACT This research advances methods for discovering equations in dynamical systems, potentially improving the interpretability and accuracy of AI models in scientific applications.

RANK_REASON The cluster contains an academic paper detailing a new methodology for equation discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Bayesian Tree-Adjoining Grammars Enhance Equation Discovery

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The cluster contains an academic paper detailing a new methodology for equation discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Christopher A. Lindley, Nikolaos Dervilis, Keith Worden ·

    Equation discovery with Bayesian tree-adjoining grammars

    arXiv:2609.31368v1 Announce Type: new Abstract: Tree-Adjoining Grammars (TAGs) have recently been introduced to Nonlinear System Identification (NLSI) as a means of encoding an entire model class as a finite set of grammatical rules, from which candidate models are assembled as t…

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