Physics-constrained AI boosts satellite weather accuracy

FAYETTEVILLE, GA, UNITED STATES, January 29, 2026 /EINPresswire.com/ -- Accurate atmospheric temperature profiles are ...

AI sharpens satellite eyes on air pollution

A new artificial intelligence approach combines deep learning with physical modeling to extract detailed aerosol properties from complex satellite observations. By learning how light intensity and ...

Chungnam National University Develops AI Model to Accelerate Defect-Based Material Design

Researchers at Chungnam National University have developed a deep learning method that predicts stable defect configurations in nematic liquid crystals in milliseconds rather than hours. This rapid ...

AI model accelerates defect-based material design

Across the physical world, many intricate structures form via symmetry breaking. When a system with inherent symmetry ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
Frontiers

Automated predictive framework using AI and deep learning approaches for early detection and classification of liver cancer

1 Department of General Surgery, Shangyu People’s Hospital of Shaoxing, Shaoxing, Zhejiang, China 2 Center of Gallbladder Disease, Shanghai East Hospital, Institute of Gallstone Disease, School of ...
InfoWorld

Meta’s SPICE framework pushes AI toward self-learning without human supervision

The new reinforcement learning system lets large language models challenge and improve themselves using real-world data instead of curated training sets. Meta researchers have unveiled a new ...
Frontiers

A deep-learning framework to detect green tide from MODIS images

Introduction: Monitoring Ulva prolifera blooms over the long term is crucial for maintaining marine ecological balance. MODIS images, with their wide spatial coverage, high temporal resolution, and ...
IEEE

A Model-Free Deep Reinforcement Learning Framework for Efficiency Optimization of Three-Phase Dual Active Bridge Converters

Abstract: The three-phase dual active bridge converter is widely used in high-power applications due to its high power density, bidirectional power flow, and soft-switching capability. However, ...