Efficient Probabilistic Prediction and Uncertainty Quantification of ...

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall ...

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical. Cyclone–Driven Storm Tides and Inundation. WILLIAM J. PRINGLE ...

Efficient Probabilistic Prediction and Uncertainty Quantification of ...

This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone– ...

Efficient Probabilistic Prediction and Uncertainty Quantification of ...

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-Driven Storm Tides and Inundation · Pringle, William J. · Burnett, Zachary ...

Efficient Probabilistic Prediction and Uncertainty Quantification of ...

The maximum water surface elevation is most sensitive to perpendicular track offset errors for all three storms. Laura is also highly sensitive ...

Efficient Probabilistic Prediction and Uncertainty Quantification of ...

Title: Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation · Argonne National ...

(PDF) Efficient Probabilistic Prediction and Uncertainty ...

We perturb forecasts of hurricane track, intensity, and size according to quasi-random low-discrepancy Korobov sequences of historical forecast ...

Efficient Probabilistic Prediction and Uncertainty Quantification of ...

Request PDF | Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation | This ...

Uncertainty Quantification and Probabilistic Forecasting

For national security and air quality, reliable uncertainty quantification is central to cost-effective decision-making, for adopting efficient strategies on ...

Uncertainty quantification for probabilistic machine learning in earth ...

Abstract:Unreliable predictions can occur when using artificial intelligence (AI) systems with negative consequences for downstream ...

Probabilistic deep learning methodology for uncertainty ...

In this paper, we present a new probabilistic deep leaning methodology for uncertainty quantification of multi-component systems' RUL.

Fast Calibrated Explanations: Efficient and Uncertainty-Aware ...

These core elements include local feature importance with calibrated predictions, both of which retain uncertainty quantification. While the new ...

Probabilistic prediction methods for nonlinear systems with ...

The consideration of uncertainties of the states further increases the computational effort and thus efficient approximations of both the distributions and the ...

Recent advances in deep learning for traffic probabilistic prediction

The probabilistic approach can be further enriched by Ensemble Learning (EL) and Prediction Intervals (PIs) to integrate the predictions from ...

Data space inversion for efficient uncertainty quantification using an ...

Generally, Bayesian methods are applied to models so that the uncertainties associated with predictions of management interest can be quantified ...

Atmospheric Sciences - EarthArXiv Preprints

Efficient Probabilistic Prediction and Uncertainty Quantification of Hurricane Surge and Inundation. William James Pringle, Zachary R Burnett, Khachik ...

Towards Calibrated, Sharp, and Interpretable Probabilistic Prediction

Predicting the future has always been a fundamental aspiration of humankind. In the era of big data, the sheer volume, diversity, and scale of data pose ...

Modern Monte Carlo methods for efficient uncertainty quantification ...

Methods in probabilistic UQ framework can be typically categorized into two groups: nonsampling methods and sampling-based methods. A typical ...

Uncertainty quantification for probabilistic machine learning in earth ...

It is amenable to integration with any prediction model and dataset, irrespective of its statistical distribution, while adhering to a pre-specified confidence ...

Probabilistic Load Forecasting With Reservoir Computing

For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification. This work ...