6 citations · 6 across the 3 of their papers we have counts for
3 papers
Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides
Lianfa Li, Roxana Khalili, Frederick Lurmann +12
Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantl…
Quantile Extreme Gradient Boosting for Uncertainty Quantification
Xiaozhe Yin, Masoud Fallah-Shorshani, Rob McConnell +3
As the availability, size and complexity of data have increased in recent years, machine learning (ML) techniques have become popular for modeling. Predictions resulting from apply…
Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction
Yijun Lin, Yao-Yi Chiang, Meredith Franklin +2
Many scientific prediction problems have spatiotemporal data- and modeling-related challenges in handling complex variations in space and time using only sparse and unevenly distri…