In lattice φ⁴ theory, Fourier modes remain marginally Gaussian but develop size-dependent couplings that cause independent Gaussian models to fail, defining three regimes where more expressive models become necessary.
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Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.
PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.
citing papers explorer
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When Independent Gaussian Models Break Down: Characterizing Regime-Dependent Modeling Failures in $\phi^4$ Theory
In lattice φ⁴ theory, Fourier modes remain marginally Gaussian but develop size-dependent couplings that cause independent Gaussian models to fail, defining three regimes where more expressive models become necessary.
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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation
Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.
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PISP: Projected-Space Inference of Stellar Parameters
PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.