Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI ...

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...

The paper shows how artificial intelligence can transform wastewater treatment by making plants smarter, more ...

National precipitation forecasting has for decades been hamstrung by static and inadequate climate models, but new tools are ...

Pharaoh saw two dreams, which Joseph recognized as structurally equivalent and carrying the same message. A stable system ...

Scientists have developed a floating PV digital twin system, trained on data from 155 physical experiments, using a two-tier artificial neural network (ANN) with a high-fidelity model and a ...

In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

Maintaining adequate CBF is crucial for astronauts' cognitive function during long-duration microgravity, but real-time monitoring in space is ...

A research paper by scientists from Beihang University proposed a machine learning (ML)-driven cerebral blood flow (CBF) prediction model, featuring multimodal imaging data integration and an ...

Artificial intelligence (AI) is increasingly transforming computational mechanics, yet many AI-driven models remain limited by poor interpretability, weak generalization, and insufficient physical ...

Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...