30 citations · 46 across the 4 of their papers we have counts for
4 papers
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…
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…
Improving seasonal forecast using probabilistic deep learning
Baoxiang Pan, Gemma J. Anderson, AndrE Goncalves +3
The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting sy…
Surrogate sea ice model enables efficient tuning
Kelly Kochanski, Ivana Cvijanovic, Donald Lucas
Predicting changes in sea ice cover is critical for shipping, ecosystem monitoring, and climate modeling. Current sea ice models, however, predict more ice than is observed in the…