Machine Learning Surrogates Slash the Cost of Sensitivity Analysis in Hydrology
Machine learning surrogates reproduce classical variance-based sensitivity results for hydrologic models at a fraction of the computational cost.
Machine learning surrogates reproduce classical variance-based sensitivity results for hydrologic models at a fraction of the computational cost.
A cross-sectional study of 447 health education students at one university found that higher gender-perception scores were associated with lower ...
A transformer-based deep-learning pipeline called ProFormer classifies single-cell and plasma proteomes directly from raw mass-spectrometry fingerprints, outperforming CNNs and enabling ...
A machine learning study of 601 young adults with repeated nonsuicidal self-injury identified self-injury frequency, mood disorder diagnoses, and low ...
A machine learning study of 825 older Chinese adults with diabetes found that an XGBoost model could identify high-risk depressive ...
Using SHAP, ALE, and LIME explainable AI techniques on daily data from 2008 to 2025, researchers show that oil market ...
An explainable AI framework has quantified how atmospheric circulation, soil moisture, and rising CO2 each contribute to summer heat extremes ...
A machine learning study of junior high students in Shanghai finds that negative low-arousal academic emotions are the strongest predictor ...
An explainable machine learning analysis of 2,612 S&P 1500 firm-year observations finds that company size is the dominant predictor of ...
A machine learning analysis of more than 8,400 Canadian adults with MASLD shows that mortality risk predictors differ sharply by ...
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© 2026 Scienmag - Science Magazine