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FluxDisco framework uses Monte Carlo Graph Search for physics-informed symbolic regression

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Researchers have introduced FluxDisco, a novel physics-informed framework designed for symbolic regression in stoichiometric dynamical systems. This method specifically targets flux-based ODE systems by incorporating known stoichiometry to constrain the search space and ensure physical consistency. FluxDisco adapts the Monte Carlo Graph Search algorithm to discover governing equations and accurately recover dynamics from noisy data across various physical and biological systems. AI

IMPACT This research advances methods for discovering governing equations in physical systems, potentially improving the interpretability and accuracy of scientific modeling.

RANK_REASON The cluster contains a single academic paper detailing a new method for symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

FluxDisco framework uses Monte Carlo Graph Search for physics-informed symbolic regression

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The cluster contains a single academic paper detailing a new method for symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Cassandra Durr (Lancaster University), Alvaro K\"ohn-Luque (University of Oslo), Chris Jewell (Lancaster University), Lloyd A. C. Chapman (Lancaster University) ·

    FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search

    arXiv:2609.05207v1 Announce Type: cross Abstract: Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing interpretability and predictive accuracy. However, standard methods often produce expressions that violate known physical l…

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