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.
Parameter identification for symbolic regression using nonlinear least squares , volume =
6 Pith papers cite this work, alongside 110 external citations. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
FunctionEvolve recovers 107 exact symbolic forms out of 129 synthetic tasks (82.9% SA@50) by using expression-tree structure for evolutionary search, parent selection, mutation, and coefficient scoring with LLMs.
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.
Merging real-valued GOMEA with GP-GOMEA enables simultaneous optimization of constants and expression structure, generally outperforming other constant-handling techniques in symbolic regression.
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
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.
citing papers explorer
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Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations
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.
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FunctionEvolve: Structure-Guided Symbolic Regression with LLMs
FunctionEvolve recovers 107 exact symbolic forms out of 129 synthetic tasks (82.9% SA@50) by using expression-tree structure for evolutionary search, parent selection, mutation, and coefficient scoring with LLMs.
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Towards symbolic regression for interpretable clinical decision scores
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.
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Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression
Merging real-valued GOMEA with GP-GOMEA enables simultaneous optimization of constants and expression structure, generally outperforming other constant-handling techniques in symbolic regression.
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Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions
Post-selection with DL or FBF after multi-objective GP search improves test-set performance over AIC/BIC baselines on noisy synthetic and real regression tasks, while using DL directly as fitness often causes premature convergence to overly simple models.
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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.