We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …

Climate science has been revolutionized by the combined effects of an exponential growth
in computing power, which has enabled more sophisticated and higher-resolution …

Pre-trained machine learning (ML) models have shown great performance for awide range
of applications, in particular in natural language processing (NLP) and computer vision (CV) …

The reconstruction of complex time-evolving fields from sensor observations is a grand
challenge. Frequently, sensors have extremely sparse coverage and low-resource …

We make connections between complexity of training of physics-informed neural networks
(PINNs) and Kolmogorov n-width of the solution. Leveraging this connection, we then …

Recently, a growing number of studies have used machine learning (ML) models to
parameterize computationally intensive subgrid‐scale processes in ocean models. Such …

We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic
pretraining approach for physical surrogate modeling. MPP involves training large surrogate …

This paper introduces the paradigm of “in-context operator learning” and the corresponding
model “In-Context Operator Networks” to simultaneously learn operators from the prompted …

We demonstrate how incorporating physics constraints into convolutional neural networks
(CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy …

Klaus Hasselmann's revolutionary intuition in climate science was to use the stochasticity
associated with fast weather processes to probe the slow dynamics of the climate system …