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.
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 ...
Rose Yu has a plan for how to make AI better, faster and smarter — and it’s already yielding results. When she was 10 years old, Rose Yu got a birthday present that would change her life — and, ...
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 ...
Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...
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 ...
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 ...