Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular ...

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 ...

Researchers at The University of Manchester have built a machine-learning model that prevents simulated molecules from flying ...

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

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 ...

Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

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 ...