Forbes

Hybrid Quantum: Classical Architectures For Machine Learning

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...
Phys.org

Automating microfluidic chip design: Hybrid approach combines machine learning with fluid mechanics

Researchers led by Assoc. Prof. Dr. Savaş Taşoğlu from the Department of Mechanical Engineering at Koç University have developed a new, open-access and machine learning-assisted design tool aimed at ...
AZoNano

Machine Learning Reveals Hidden Nanophotonic Resonances In Silicon-Gold Nanopillars

Researchers developed a hybrid UMAP-HDBSCAN-SVM machine learning workflow to rapidly classify low-loss STEM-EELS spectrum ...
Frontiers

Advancing Machine Learning for Space Weather Forecasting: Integrating Solar Observations, Geomagnetic Storm Prediction, and GNSS Ionospheric Forecasting

Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...
Frontiers

Numerical foundations for scalable statistical and machine learning methods

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...