Results 71 to 80 of about 10,063 (188)

Epistemic Uncertainty Analysis and Robust Optimization of a Second‐Generation Solvent‐Based Post‐Combustion Carbon Capture Process

Engineering Reports, Volume 8, Issue 4, April 2026.
Parametric uncertainty in the PZ/AFS post‐combustion carbon‐capture process model compromises the reliability of process designs extracted via traditional, deterministic optimization methods. We identify the twelve parameters exhibiting highest impact on capture and costs, and mitigate the design risk stemming from those via formulating and solving a ...
Ilayda Akkor, Shachit S. Iyer, John Dowdle, Le Wang, Chrysanthos E. Gounaris   +4 more
wiley   +1 more source

Spatio‐Temporal Modelling of Extreme Induced Seismicity in the Presence of An Evolving Measurement Network

Environmetrics, Volume 37, Issue 3, April 2026.
ABSTRACT Earthquakes induced by injecting or extracting gas from underground reservoirs can pose a significant hazard to surrounding infrastructure and populations. Safeguarding against future seismic hazards requires accurate models for the upper tail of the earthquake magnitude distribution that are able to represent various intervention strategies ...
Conor Murphy, Jonathan A. Tawn, Zak Varty, Ross Towe, Peter M. Atkinson   +4 more
wiley   +1 more source

Identifying Drivers of Predictive Aleatoric Uncertainty

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing model limitations and enhancing transparent decision-making.
Iversen, Pascal, Witzke, Simon, Baum, Katharina, Renard, Bernhard Y.   +3 more
openaire   +2 more sources

Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 4, April 2026.
ABSTRACT This review examines the application of machine learning (ML) in physiologically based pharmacokinetic (PBPK) modeling through improved prediction of input parameters, particularly via quantitative structure–activity relationship (QSAR) models, for absorption, distribution, metabolism, and excretion (ADME) properties across drug development ...
Xinyue Chen, Zhoumeng Lin
wiley   +1 more source

Evaluating Aleatoric Uncertainty via Conditional Generative Models

, 2022
Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learning applications. Previous research on aleatoric uncertainty estimation mainly targets closed-formed conditional densities or variances, which requires strong restrictions on
Huang, Ziyi, Lam, Henry, Zhang, Haofeng
openaire   +2 more sources

Deformation and Earthquake Potential on the North America–Caribbean–Cocos Plate Boundary System in Guatemala

Journal of Geophysical Research: Solid Earth, Volume 131, Issue 4, April 2026.
Abstract In Guatemala, the Cocos, North American, and Caribbean plates interact to create a region of high seismic risk. Previous analyses of crustal faults in the country have been overly simplified, creating discrepancies between geologic and geodetic slip rate models.
Jeremy Maurer, Andreas Eckert, Qiaoqi Sun, Jonathan Obrist‐Farner   +3 more
wiley   +1 more source

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

Uncertainty-Aware Time Series Anomaly Detection

Future Internet
Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty ...
Paul Wiessner, Grigor Bezirganyan, Sana Sellami, Richard Chbeir, Hans-Joachim Bungartz   +4 more
doaj   +1 more source

Building Uncertainty Models on Top of Black-Box Predictive APIs

IEEE Access, 2020
With the commoditization of machine learning, more and more off-the-shelf models are available as part of code libraries or cloud services. Typically, data scientists and other users apply these models as “black boxes” within larger ...
Axel Brando, Clara Torres-Latorre, Jose A. Rodriguez-Serrano, Jordi Vitria   +3 more
doaj   +1 more source