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 ...

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

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 ...

A team of EPFL researchers has developed an AI algorithm capable of modelling complex dynamical processes while adhering to ...

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 ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...

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 ...

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 ...