Researchers employ machine learning to more accurately model the boundary layer wind field of tropical cyclones. Conventional approaches to storm forecasting involve large numerical simulations run on ...

A new research model called PiGRAND merges physics guidance with graph neural diffusion to predict and control AM processes.

This Collection supports and amplifies research related to SDG3, SDG9 and SDG10. Physics-Informed Machine Learning (PI-ML) combines principles from physics- and biology-based modeling with data-driven ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

Optical coherence tomography (OCT) is an imaging technology that can non-invasively generate cross-sectional images of tissue. OCT is widely used in eye clinics to diagnose and manage retinal diseases ...

John Hopfield and Geoffrey Hinton were awarded the 2024 Nobel Prize in physics on Tuesday for their contributions to machine learning. Their research, which draws from statistical physics, helped ...

STOCKHOLM — John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in physics Tuesday for discoveries and inventions that formed the building blocks of machine learning. "This year's two Nobel ...

As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...

Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...