30 citations · 30 across the 3 of their papers we have counts for
5 papers
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport
M. Giselle Fernández-Godino, Wai Tong Chung, Akshay A. Gowardhan +4
High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes the…
One-Dimensional, One-Phase and Two-Phase Eulerian Explicit Shock Tube Simulation Code
M. Giselle Fernández-Godino
In this work, a one-dimensional simulation code was developed for both single-phase and two-phase systems, focusing on time-dependent Euler equations for gas and particles. These e…
Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications
Qingkai Kong, Andrea Chiang, Ana C. Aguiar +3
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to…
Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture
Cristina Garcia-Cardona, M. Giselle Fernández-Godino, Daniel O'Malley +1
Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive. The cost increases even more if multiple…
Accelerating High-Strain Continuum-Scale Brittle Fracture Simulations with Machine Learning
M. Giselle Fernández-Godino, Nishant Panda, Daniel O'Malley +4
Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this mechanism at the continuum level is com…