The simulation of high-energy particle collisions is an essential task in high-energy nuclear and particle physics and high-energy astroparticle physics. However, although data sets from both fields ...

Precise modeling of detector energy response is crucial for next-generation neutrino experiments, which present computational challenges due to the lack of analytical likelihoods. We propose a ...

Large language models have already transformed software engineering, for better or worse. Now, so-called large physics models are also starting to transform design engineering. These tools are ...

Scientists found that transfer learning can make the search for new physics in the universe much faster, slashing the need ...

Hybrid climate modeling has emerged as an effective way to reduce the computational costs associated with cloud-resolving models while retaining their accuracy. The approach retains physics-based ...

Electrochemical impedance spectroscopy (EIS) provides valuable insights into the physical processes within batteries – but how can these measurements directly inform physics-based models? In this ...

Over the past decade or so, foundation models have emerged as the dominant paradigm for interacting with language, images, and code. Large Language Models (LLMs) can generate text. Vision models can ...

The use of better-quality data can improve the business case for a number of solar projects. Image: Photon Group. The integrity of a PV project largely depends on the quality of the solar, ...

A recent study in Science Advances shows that AI-based weather models struggle with extreme events, such as heat waves, heavy ...

Weather forecasting is another aspect of modern life that artificial intelligence is transforming. Models like GraphCast, Pangu-Weather, and Fuxi are already better than traditional physics-based ...