Multi-dimensional fluid flow plays a paramount role in the explosions of massive stars as
core-collapse supernovae. In recent years, three-dimensional (3D) simulations of these …

Binary neutron star mergers observations are a unique way to constrain fundamental
physics and astrophysics at the extreme. The interpretation of gravitational-wave events and …

Completely revised text focuses on use of spectral methods to solve boundary value,
eigenvalue, and time-dependent problems, but also covers Hermite, Laguerre, rational …

We present an introduction to the spectral element method, which provides an innovative
numerical approach to the calculation of synthetic seismograms in 3-D earth models. The …

We present a significantly improved data‐driven global weather forecasting framework using
a deep convolutional neural network (CNN) to forecast several basic atmospheric variables …

A finite-volume dynamical core with a terrain-following Lagrangian control-volume
discretization is described. The vertically Lagrangian discretization reduces the …

We use a spectral-element method to simulate seismic wave propagation throughout the
entire globe. The method is based upon a weak formulation of the equations of motion and …

Recent progress in numerical methods and computer science allows us today to simulate
the propagation of seismic waves through realistically heterogeneous Earth models with …

Abstract Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural …

We simulate global seismic wave propagation based upon a spectral-element method. We
include the full complexity of 3-D Earth models, ie lateral variations in compressional-wave …