A physics informed machine learning model predicts thermal conductivity from infrared images in milliseconds, enabling fast, ...

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 ...

Recent developments in machine learning techniques have been supported by the continuous increase in availability of high-performance computational resources and data. While large volumes of data are ...

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 used generative AI to develop a physics-informed technique to classify phase transitions in materials or physical systems that is much more efficient than existing machine-learning ...

Here's the case for constraint-aware intelligence in the AI 2.0 era.

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 ...