Auteur/autrice : Redouane Lguensat

Journal club meetings

Next meetings

  • [17-06-2020] ​ Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity (Patel et al. 2021) 
    https://aip.scitation.org/doi/10.1063/5.0042598 
    Discussion led by: Julien Brajard

Past meetings

  • [29-04-2020] ​ Improving El Nino Forecasts with Graph Neural Networks (Cachay et al. 2021) 
https://arxiv.org/abs/2104.05089 
Discussion led by: Joana Roussillon

    • [29-04-2020] Machine learning accelerated computational fluid dynamics (Kochkov et al. 2021) https://arxiv.org/abs/2102.01010
Discussion led by: Hugo Frezat

    • [18-03-2020] Using machine learning to correct model error in data assimilation and forecast applications (Farchi et al. 2020) https://arxiv.org/abs/2010.12605
Discussion led by: Alban Farchi
    • [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
    • [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) Paper: 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