Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …

Modern practice for training classification deepnets involves a terminal phase of training
(TPT), which begins at the epoch where training error first vanishes. During TPT, the training …

We describe the new field of the mathematical analysis of deep learning. This field emerged
around a list of research questions that were not answered within the classical framework of …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

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Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Convolutional neural networks have been extremely successful in the image recognition
domain because they ensure equivariance with respect to translations. There have been …

Supervised operator learning is an emerging machine learning paradigm with applications
to modeling the evolution of spatio-temporal dynamical systems and approximating general …