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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8 Pith papers cite this work, alongside 10,417 external citations. Polarity classification is still indexing.
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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
2026 8roles
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background 2representative citing papers
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.
A bottleneck neural network trained on ~700 shallow nanoindentations from three steels estimates high-load reference hardness on a fourth steel with ~0.47 GPa RMSE, while the abstract's 0.28 GPa physics-constrained result is absent from the body.
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.
citing papers explorer
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A spectral-subspace-augmented POD-Galerkin method for parametrized PDEs with limited snapshot data
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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Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
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.
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A Technical Typology of AI Systems in Public Administration
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.
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Bridging Phase-Field Model and Deep Learning for Predicting 2D and 3D Microstructure Evolution in Ternary Alloys
Hybrid phase-field and attention-based deep learning model predicts microstructure evolution in ternary alloys up to 400 timesteps with generalization to new compositions.
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When Independent Gaussian Models Break Down: Characterizing Regime-Dependent Modeling Failures in $\phi^4$ Theory
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.
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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Constrained Neural Network
A bottleneck neural network trained on ~700 shallow nanoindentations from three steels estimates high-load reference hardness on a fourth steel with ~0.47 GPa RMSE, while the abstract's 0.28 GPa physics-constrained result is absent from the body.
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Quantifying Surface Heterogeneity Across Asteroid (101955) Bennu using Candidate Site Remote Sensing Data
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.
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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.