Results 81 to 90 of about 7,687 (182)

Stochastic Weather Generation for Scenario‐Neutral Impact Assessments Using Simulation‐Based Inference

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Scenario‐neutral and robust adaptation methods assess the vulnerability of climate‐sensitive systems against a range of plausible climate conditions, independent of the socioeconomic scenarios typically used in climate modeling. Stochastic weather generators facilitate such analyses by enabling fast and flexible simulation of meteorological ...
Brian Groenke, Jakob Wessel, Peter Miersch, Nadja Klein, Jakob Zscheischler   +4 more
wiley   +1 more source

Leveraging Atmospheric Information Across Scales for Local Temperature Forecasting Using a Novel Multi‐Scale Perception Network

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Regional weather forecasting systems typically depend on boundary conditions from global models to represent large‐scale atmospheric processes, while such coupling increases complexity and hinders end‐to‐end optimization for specific target locations. Here, we propose the Multi‐scale Perception Network (MPN), a unified deep learning model that
Han Wang, Yilin Chen, Jiachuan Yang
wiley   +1 more source

Am I confused or is this confusing?: Deep ensembles for ENSO uncertainty quantification

Machine Learning. Earth
Faithful uncertainty quantification (UQ) is paramount in high stakes climate prediction. Deep ensembles, or ensembles of probabilistic neural networks, are state of the art for UQ in machine learning (ML) and are growing increasingly popular for weather ...
Devin M McAfee, Elizabeth A Barnes
doaj   +1 more source

Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation. [PDF]

J Med Imaging (Bellingham), 2022
Jones CK, Wang G, Yedavalli V, Sair H.
europepmc   +1 more source

Aleatoric and Epistemic Uncertainty in Conformal Prediction

Recently, there has been a particular interest in distinguishing different types of uncertainty in supervised machine learning (ML) settings (Hullermeier and Waegeman, 2021). Aleatoric uncertainty captures the inherent randomness in the data-generating process.
Nguyen (Ed.), Khuong An, Luo (Ed.), Zhiyuan, Papadopoulos (Ed.), Harris, Löfström (Ed.), Tuwe, Carlsson (Ed.), Lars, Boström (Ed.), Henrik   +5 more
openaire   +3 more sources

Incoherence: A Generalized Measure of Complexity to Quantify Ensemble Divergence in Multi-Trial Experiments and Simulations

Entropy
Complex systems pose significant challenges to traditional scientific and statistical methods due to their inherent unpredictability and resistance to simplification.
Timothy Davey
doaj   +1 more source

Bayesian Deep Learning for Uncertainty-Aware Analysis and Predictive Modeling of Graphene and MoS₂-Coated Terahertz Biosensors for Biomarker Detection in AML

Applied Sciences
In this paper, we propose a Bayesian Deep Learning (BDL) framework to model uncertainty and predict the performance of terahertz (THz) biosensors with a graphene and molybdenum disulfide (MoS2) coating for AML biomarker detection.
Arcel Kalenga Muteba, Kingsley A. Ogudo
doaj   +1 more source

Toward a New Flood Assessment Paradigm: From Exceedance Probabilities to the Expected Maximum Floods and Damages

Water Resources Research
To assess flood risks, we seek to estimate the probability distribution of the worst possible single‐event over a contiguous period of N years rather than the cumulative losses expected over a planning horizon.
E. Todini, P. Reggiani
doaj   +1 more source

Context-aware uncertainty modeling for pedestrian intention detection in urban environments [PDF]

International Journal of Electronics and Telecommunications
The present study investigates the application of uncertainty modelling for the purpose of detecting pedestrian intentions in contexts pertaining to autonomous driving.
Yusuf Yesilyurt, Marek Woda
doaj   +1 more source

Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning

IEEE Access
This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedasticity.
Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton   +2 more
doaj   +1 more source