7 citations · 10 across the 4 of their papers we have counts for
4 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…
Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon Storage
Hewei Tang, Qingkai Kong, Joseph P. Morris
Deep learning-based surrogate models have been widely applied in geological carbon storage (GCS) problems to accelerate the prediction of reservoir pressure and CO2 plume migration…
Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination
Qingkai Kong, Ruijia Wang, William R. Walter +3
This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and…
Predicting Wind-Driven Spatial Deposition through Simulated Color Images using Deep Autoencoders
M. Giselle Fernández-Godino, Donald D. Lucas, Qingkai Kong
For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is…