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 case study in aerospace manufacturing provides an overview of how physics-informed digital twin systems transform robotics processes—from adaptive process planning and real-time process monitoring ...

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

Less instrumentation. More insight. Physics-informed virtual sensors are shifting condition monitoring from isolated pilots ...

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 observational track of Typhoon "Danas" (solid line) along with forecasted paths (dashed lines) depicted on the FY-4B satellite visible light imagery at 08:00 BST on July 6, 2025. The dashed lines ...

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 ...

Demis Hassabis, Google Deepmind CEO, just told the AI world that ChatGPT's path needs a world model. OpenAI and Google and ...

In many AI applications, being “mostly right” is acceptable. Industrial equipment is not one of them. Here, machines are ...