Four parameters suffice to describe dust attenuation curve diversity in TNG simulations, yielding a new symbolic-regression model that recovers curves and fluxes better than existing parameterizations while linking parameters to SFR surface density, metallicity, and geometry.
Operon c++: An efficient genetic programming framework for symbolic regression
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Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
A method called the degeneracy distillery uses symbolic transformations to flatten the Fisher information matrix globally from simulations alone, identifying independent parameter combinations and reducing neural posterior estimation simulation budgets by up to 10x.
GPU fitness evaluation for GP-GOMEA boosts throughput, improves benchmark results especially on large datasets, and allows reliable regression of large Feynman equations within hours.
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.
A data-driven framework applies population-based bio-inspired algorithms to recalibrate and evolve a prostate cancer-specific comorbidities index, reporting up to 0.1 improvement in concordance index over the Charlson index.
Symbolic regression provides an interpretable way to extract mathematical relationships from data for scientific discovery and surrogate modeling in the physical sciences.
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Symbolic Emulators for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
Symbolic emulators approximate key Lambda CDM functions to 0.001-0.05% accuracy across relevant redshifts and Omega_m values, enabling faster 3x2pt inference with consistent results.