Global medium-range weather forecasting is critical to decision-making across many social
and economic domains. Traditional numerical weather prediction uses increased compute …

Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …

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 …

Coastal flood damage is primarily the result of extreme sea levels. Climate change is
expected to drive an increase in these extremes. While proper estimation of changes in …

We present FengWu, an advanced data-driven global medium-range weather forecast
system based on Artificial Intelligence (AI). Different from existing data-driven weather …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Modeling weather and climate is an essential endeavor to understand the near-and long-
term impacts of climate change, as well as to inform technology and policymaking for …

Extreme weather amplified by climate change is causing increasingly devastating impacts
across the globe. The current use of physics-based numerical weather prediction (NWP) …

Partial differential equations (PDEs) are central to describing complex physical system
simulations. Their expensive solution techniques have led to an increased interest in deep …

Abstract We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …