Researchers have developed a Bayesian symbolic regression method to uncover missing physics in complex systems. This approach uses Reversible Jump Markov Chain Monte Carlo to sample from the posterior distribution of expression trees, providing uncertainty quantification for discovered equations. The methodology was demonstrated on a predator-prey model and a bioreactor case study, showing how experimental design can reduce model uncertainty. AI
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IMPACT Provides a method for interpretable discovery of underlying equations in scientific models, potentially improving AI's role in scientific research.
RANK_REASON Academic paper detailing a new methodology for symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]