Physics

Imagine this: you’re driving, clouds are gathering, and your weather app says “heavy rain in 15 minutes” — but there are no local radars, and it gets it wrong. Sounds familiar? That’s exactly the kind of problem tackled by the new research paper Precipitation nowcasting of satellite data using physically conditioned neural networks (by Antônio Catão et al.). The authors present a model that can forecast precipitation using only satellite data, powered by a neural network that’s conditioned by physics. In short: less “black box” magic, more scientific reasoning — and better forecasts where radar coverage is weak or nonexistent. ...

Modern science, especially in the field of high-energy physics, generates unimaginable amounts of data. Experiments like the LCLS-II free-electron laser (FEL) at the SLAC National Accelerator Laboratory produce terabytes of data per second. Transmitting and storing all of it is impractical. The solution is to intelligently select data in real-time, right at the source. The publication “Neural Network Acceleration on MPSoC board: Integrating SLAC’s SNL, Rogue Software and Auto-SNL” is a fascinating case study of how to achieve this using artificial intelligence and specialized hardware. ...

Can a machine discover the laws of physics by itself—like Newton, but without the apple and without writing the equation by hand? In June 2025, a new method called H-FEX (Hamiltonian Finite Expression) was published. It doesn’t just predict system behavior—it writes down the math behind it. And crucially, in a form humans can understand. It’s a form of symbolic learning, increasingly popular over black-box neural networks that work, but don’t tell us why. ...