The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

We consider solving partial differential equations (PDEs) with Fourier neural operators
(FNOs), which operate in the frequency domain. Since the laws of physics do not depend on …

Downscaling methods are critical in efficiently generating high‐resolution atmospheric data.
However, state‐of‐the‐art statistical or dynamical downscaling techniques either suffer from …

Seismic waveform modeling is a powerful tool for determining earth structure models and
unraveling earthquake rupture processes, but it is usually computationally expensive. We …

The rise of machine learning (ML) has significantly advanced the field of coastal
oceanography. This review aims to examine the existing deficiencies in numerical …

Numerical simulations in climate, chemistry, or astrophysics are computationally too
expensive for uncertainty quantification or parameter-exploration at high-resolution …

Wetlands play a critical role in maintaining the global climate, regulating the hydrological
cycle, and protecting human health. However, they are rapidly disappearing due to human …

The forthcoming wave of progress in oceanographic technology is the digital twin of the
ocean, a concept that integrates marine big data and artificial intelligence (AI). This …