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MIT engineers have developed a machine-learning algorithm called Extreme Event Aware that generates plausible extreme event scenarios without relying on historical extreme data, aiding in risk assessment for areas like weather and finance.
TailBooster is a dual-layer generative framework that synthesizes operationally valid extreme air-transport events using statistical tail extraction and autoencoder-based cleaning, significantly improving extreme-event prediction accuracy.
The paper proposes Neural Tangent Kernel-based uncertainty quantification for deterministic deep learning weather models, achieving sharper adaptive prediction intervals during extreme events without retraining.
Introduces Q-srdrn, a multi-quantile super-resolution network using pinball loss to improve extreme precipitation downscaling, achieving dramatic detection rate gains for heavy rainfall events while maintaining overall accuracy.