Next meetings
[18-02-2020] Taking climate model evaluation to the next level (Eyring et al. 2019) https://www.nature.com/articles/s41558-018-0355-y
Discussion led by: Pierre Le Bras
Past meetings
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[17-12-2020] DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations (Barth et al. 2020) https://gmd.copernicus.org/articles/13/1609/2020/
Discussion led by: Anastase Charantonis - [26-11-2020] Process-based climate model development harnessing machine learning: II. model calibration from single column to global (Hourdin et al. 2020) https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002225
Discussion led by: Frederic Hourdin
- [15-10-2020] ExGAN: Adversarial Generation of Extreme Samples (Bhatia et al. 2020)
Discussion led by: Maxime Beauchamp
- [17-09-2020] Process-based climate model development harnessing machine learning: I. a calibration tool for parameterization improvement (Couvreux et al. 2020)
Paper:
https://www.essoar.org/doi/10.1002/essoar.10503597.1
Discussion led by: Redouane Lguensat
- [16-07-2020] A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets (Anantrasirichaia et al. 2019)
Paper:
https://arxiv.org/pdf/1905.07286.pdf
Discussion led by: Sophie Giffard-Roisin
- [18-06-2020] Up to two billion times acceleration of scientific simulations with deep neural architecture search (Kasim et al. 2020) Papers:
https://arxiv.org/pdf/2001.08055.pdf
Discussion led by: Julie Deshayes
- [28-05-2020] Machine learning and the physical sciences (Carleo et al. 2019) + Deep learning and process understanding for data-driven Earth system science (Reichstein et al. 2019) Papers:
https://arxiv.org/abs/1903.10563 + https://www.nature.com/articles/s41586-019-0912-1
Discussion led by: Maike Sonnewald
- [16-04-2020] Universal Differential Equations for Scientific Machine Learning (Rackauckas et al. 2020)
Paper: https://arxiv.org/pdf/2001.04385.pdf
Discussion led by: Redouane Lguensat
- [19-03-2020] WeatherBench: A benchmark dataset for data-driven weather forecasting by S. Rasp, P.D. Dueben, S. Scher, J.A. Weyn, S. Mouatadid and N. Thuerey (2020)
Paper: https://arxiv.org/abs/2002.00469
Discussion led by: Julien Le Sommer
- [27-02-2020] Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence (George et al. 2020)
Paper: https://eartharxiv.org/erhy2/
Discussion led by: Julie Deshayes