Identifies conditions and explicit constructions allowing polynomial-size quantum circuits to implement geometry oracles for pseudorandom textured materials, in contrast to Grover-hard unstructured cases.
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution
3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
A PINN transfer learning framework for coal methane sorption reaches R²=0.932 on held-out data with 227% improvement over classical isotherms and identifies Monte Carlo Dropout as the best uncertainty method while ensembles degrade under shared physics constraints.
citing papers explorer
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How to make quantum cheese: efficient geometry oracles for exponentially many pseudorandom microstructures
Identifies conditions and explicit constructions allowing polynomial-size quantum circuits to implement geometry oracles for pseudorandom textured materials, in contrast to Grover-hard unstructured cases.
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Statistical Tapers for Correlation-Based Localization in Ensemble Data Assimilation
Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
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Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
A PINN transfer learning framework for coal methane sorption reaches R²=0.932 on held-out data with 227% improvement over classical isotherms and identifies Monte Carlo Dropout as the best uncertainty method while ensembles degrade under shared physics constraints.