A finite modular symmetric model generates inflation via a Coleman-Weinberg potential from vector-like quarks, with Im(τ) as inflaton and Re(τ) as heavy axion, matching cosmological observations and predicting possible isocurvature perturbations.
Regression-based inverse distance weighting with applications to computer experiments
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A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.
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Finite modular Coleman-Weinberg inflation
A finite modular symmetric model generates inflation via a Coleman-Weinberg potential from vector-like quarks, with Im(τ) as inflaton and Re(τ) as heavy axion, matching cosmological observations and predicting possible isocurvature perturbations.
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Active Learning for Manifold Gaussian Process Regression
A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.