Recurrent Neural Network Architecture Search for Geophysical Emulation

Understand

Developing surrogate geophysical models from data is a key research topic in atmospheric and oceanic modeling because of the large computational costs associated with numerical simulation methods.

• Researchers have started applying a wide range of machine learning models, in particular neural networks, to geophysical data for forecasting without these constraints.
• Constructing neural networks for forecasting such data is nontrivial, however, and often requires trial and error.
• To address these limitations, we focus on developing proper-orthogonal-decomposition-based long short-term memory networks (POD-LSTMs).