The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …

Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …

Time-dependent partial differential equations (PDEs) are ubiquitous in science and
engineering. Recently, mostly due to the high computational cost of traditional solution …

Large AI models, or foundation models, are models recently emerging with massive scales
both parameter-wise and data-wise, the magnitudes of which can reach beyond billions …

In recent years, Transformers have become the de-facto architecture for sequence modeling
on text and multi-dimensional data, such as images and video. However, the use of self …

Physics-informed neural networks (PINNs) have been popularized as a deep learning
framework that can seamlessly synthesize observational data and partial differential …

WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting
benchmark proposed by (Rasp et al., 2020, https://doi. org/10.1029/2020ms002203) …

While diffusion models can successfully generate data and make predictions, they are
predominantly designed for static images. We propose an approach for training diffusion …