Nature

Physics-driven learning for inverse problems in quantum chromodynamics

The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex observations.
Phys.org

Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
Nature

Deep learning and a changing economy in weather and climate prediction

Lives and well-beings depend on reliable weather predictions every day. With climate change, the need for better predictions of future weather extremes rises to levels where no investment seems big ...
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 ...
Hosted on MSN

Learning physics can derail some students: New research shows the best way to keep them on track

For many undergraduate students, exploring the complexities of physics for the first time, from wading through advanced mathematics, to absorbing information in a large lecture format, can be a ...
Drexel University

Learning Physics Can Derail Some Students, New Research From Drexel Shows the Best Way to Keep Them on Track

For many undergraduate students, exploring the complexities of physics for the first time, from wading through advanced mathematics, to absorbing information in a large lecture format, can be a ...