The differentiable programming paradigm is a cornerstone of modern scientific computing. It
refers to numerical methods for computing the gradient of a numerical model's output. Many …

Different approaches to using data-driven methods for subgrid-scale closure modeling have
emerged recently. Most of these approaches are data-hungry, and lack interpretability and …

Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and
economic damage globally, highlighting the need for accurate, high‐resolution modeling …

Deep learning frameworks have become powerful tools for approaching scientific problems
such as turbulent flow, which has wide-ranging applications. In practice, however, existing …

Knowledge of ocean circulation is important for understanding and predicting weather and
climate, and managing the blue economy. This circulation can be estimated through Sea …

Predicting gray swan weather extremes, which are possible but so rare that they are absent
from the training dataset, is a major concern for AI weather/climate models. An important …

High-fidelity direct numerical simulation of turbulent flows for most real-world applications
remains an outstanding computational challenge. Several machine learning approaches …

Forecasting multiscale chaotic dynamical systems with deep learning remains a formidable
challenge due to the spectral bias of neural networks, which hinders the accurate …

Machine learning (ML) techniques, especially neural networks (NNs), have shown promise
in learning subgrid-scale parameterizations for climate models. However, a major problem …

Neural network emulators have become an invaluable tool for a wide variety of climate and
weather prediction tasks. While showing incredibly promising results, these networks do not …