558 citations · 1.6k across the 8 of their papers we have counts for
8 papers
A Machine Learning Outlook: Post-processing of Global Medium-range Forecasts
Shreya Agrawal, Rob Carver, Cenk Gazen +7
Post-processing typically takes the outputs of a Numerical Weather Prediction (NWP) model and applies linear statistical techniques to produce improve localized forecasts, by inclu…
Global Extreme Heat Forecasting Using Neural Weather Models
Ignacio Lopez-Gomez, Amy McGovern, Shreya Agrawal +1
Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disrup…
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
Lasse Espeholt, Shreya Agrawal, Casper Sønderby +7
The problem of forecasting weather has been scientifically studied for centuries due to its high impact on human lives, transportation, food production and energy management, among…
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Fantine Huot, R. Lily Hu, Matthias Ihme +6
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly…
MetNet: A Neural Weather Model for Precipitation Forecasting
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek +6
Weather forecasting is a long standing scientific challenge with direct social and economic impact. The task is suitable for deep neural networks due to vast amounts of continuousl…
Machine Learning for Precipitation Nowcasting from Radar Images
Shreya Agrawal, Luke Barrington, Carla Bromberg +3
High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dra…
Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
Jiaqi Jiang, David Sell, Stephan Hoyer +3
A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices. Design methods based on iterative opti…
Learning data driven discretizations for partial differential equations
Yohai Bar-Sinai, Stephan Hoyer, Jason Hickey +1
The numerical solution of partial differential equations (PDEs) is challenging because of the need to resolve spatiotemporal features over wide length and timescales. Often, it is…