Auteur : Redouane Lguensat

Past meetings

• [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)
  https://arxiv.org/pdf/2009.08454v1.pdf

  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