Abstract: The rapid proliferation of stochastic renewable generation and electric vehicle (EV) charging loads necessitates advanced operational frameworks that reconcile probabilistic grid constraints ...

Artificial intelligence has moved rapidly from academic research into industrial practice, but its adoption in nuclear engineering remains under cautious review. This reticence is not ideological but ...

This monograph provides a rigorous overview of theoretical and methodological aspects of probabilistic inference and learning with Stein’s method. Recipes are provided for constructing Stein ...

As foundation models, including large language models and multimodal models, are increasingly deployed in complex and high-stakes settings, ensuring their safety has become more important than ever.

You're probably a little tired of reading or hearing about AI, right? Well, if that's the case, then you're in the right place because here, we're going to talk about machine learning (ML). Yes, it's ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...

Underpinnings and advantages of the scDiffEq model The new machine learning-based framework developed by the researchers models how cells change over time using neural stochastic differential ...

Interactive Brokers’ prediction markets platform ForecastEx has appointed renowned academic and forecasting expert Dr. Philip Tetlock to its Board of Directors, strengthening the prediction market’s ...

Validation of the probabilistic machine learning framework using the SpT model against the OCO-2 Level 2 data product. (A) Scatter density plots comparing XCO2 values and associated uncertainties from ...