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 ...

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 ...

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 ...

Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...

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 ...

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 ...

The human brain, with its billions of interconnected neurons giving rise to consciousness, is generally considered the most powerful and flexible computer in the known universe. Yet for decades ...