506 citations · 894 across the 7 of their papers we have counts for
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physics.flu-dyn2020★ 42 cited
Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates
Li-Wei Chen, Berkay Alp Cakal, Xiangyu Hu +1
Efficiently predicting the flowfield and load in aerodynamic shape optimisation remains a highly challenging and relevant task. Deep learning methods have been of particular intere…
physics.ao-ph2020★ 167 cited
Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench
Stephan Rasp, Nils Thuerey
Numerical weather prediction has traditionally been based on physical models of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purel…
physics.ao-ph2020★ 506 cited
WeatherBench: A benchmark dataset for data-driven weather forecasting
Stephan Rasp, Peter D. Dueben, Sebastian Scher +3
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could al…