SS-POD augments standard POD-Galerkin with a spectral-subspace partition and local POD to achieve lower out-of-sample error than either plain POD or pure spectral-Galerkin when only a handful of snapshots are available.
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cond-mat.mtrl-sci 2 astro-ph.EP 1 astro-ph.SR 1 cond-mat.stat-mech 1 cs.AI 1 cs.CY 1 math.NA 1years
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Graph-PRefLexOR fine-tunes graph-native models with GRPO to organize reasoning into phases, yielding 40-65% gains in traceable hypothesis generation and 2-3x semantic diversity on 100 materials science questions.
The paper defines five AI system categories for public administration and reports that 55% of 91 recent papers leave the system type underspecified while 31% study one type but motivate with another.
Hybrid phase-field and attention-based deep learning model predicts microstructure evolution in ternary alloys up to 400 timesteps with generalization to new compositions.
In 1D lattice φ⁴ theory, Gaussian independent Fourier mode models fail mainly from growing structured mode dependencies rather than non-Gaussian marginals, defining three regimes that mark where traditional methods remain usable.
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.
Spectral analysis of four Bennu sites reveals statistically significant heterogeneity in hydration and silicate features at 2-10 m scales, with Nightingale encompassing the full observed range.
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.
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