Next meetings
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[23-06-2022] Inferring causation from time series in Earth system sciences (Runge et al. 2019) https://www.nature.com/articles/s41467-019-10105-3
Discussion led by: Homer Durand
Past meetings
- [18-11-2021] Learning to Simulate Complex Physics with Graph Networks (Sanchez-Gonzalez et al. 2020) http://proceedings.mlr.press/v119/sanchez-gonzalez20a/sanchez-gonzalez20a.pdf ICLR 2020 paper
Discussion led by: Alex Ayet (CNRS, Gipsa-Lab) - [21-10-2021] Data-driven discovery of coordinates and governing equations (Champion et al. 2019) https://www.pnas.org/content/116/45/22445.short
Disucssion led by: V. Balaji - [17-06-2021] 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 - [29-05-2020] 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