There is growing interest in discovering interpretable, closed-form equa...
Long-term stability is a critical property for deep learning-based
data-...
Data assimilation (DA) is a key component of many forecasting models in
...
Transfer learning (TL) is becoming a powerful tool in scientific applica...
Recent years have seen a surge in interest in building deep learning-bas...
Physics-informed neural networks (PINNs) leverage neural-networks to fin...
FourCastNet, short for Fourier Forecasting Neural Network, is a global
d...
Models used for many important engineering and natural systems are imper...
There is growing interest in data-driven weather prediction (DDWP), for
...
To make weather/climate modeling computationally affordable, small-scale...
Numerical weather prediction (NWP) models require ever-growing computing...
In this paper, the performance of three deep learning methods for predic...
Convolutional neural networks (CNNs) can potentially provide powerful to...