2nd Working Group: Learning dynamics from partial and noisy observation with the help of Data Assimilation – Arthur Filoche

11 December, 10 o’clock, Salle de réunion SCAI, Jussieu.
Batiment Esclangon 1er étage

Participer à la réunion Zoom
https://us02web.zoom.us/j/89174956656

Geosciences have long-standing experience in modeling, forecasting, or estimating complex dynamical systems like the atmosphere or the ocean. Most of these models came from physical laws and are described by PDE. Usually, sparse and noisy observations of such systems are available. The first need to produce a forecast is to estimate initial conditions. This is usually done via Data Assimilation (DA), a set of methods that optimally combines a dynamical model and observations, focusing on system state estimation. In variational formalism, it’s a PDE-constrained optimization problem that requires adjoint modeling to calculate gradients. This field is very close to Machine Learning (ML) in the sense that both learn from data.

ML algorithms have demonstrated impressive results of spatiotemporal forecasting, but to do so it needs dense data which is rarely the case in earth sciences. Also, tools provided by the deep learning community based on automatic differentiation are particularly suitable for variational DA, avoiding explicit adjoint modeling.

What motivates this discussion is that physics-based model is often
incomplete and machine learning can provide a learnable class of model
while data assimilation can provide dense data.

Tenure track in Statistical learning at Ecole Polytechnique

Ecole polytechnique  is opening a tenure track position on statistical learning and artificial intelligence for energy/climate. The description is attached. See also
https://gargantua.polytechnique.fr/siatel-web/linkto/mICYYYSdehW

This is a position between the Applied Math departement  and the Meteorology  departement.We are primarily interested in applicants whose research in statistical learning and artificial intelligence shall contribute to address societal challenges in energy, sustainability and climate change (e.g. statistical learning for energy efficiency, for load curve prediction, for pricing mechanisms, for smart grid control, for load curve disaggregation, etc.). The Ecole Polytechnique offers an exceptional environment, with an adapted teaching load, as well as the support (scientific, administrative and budgetary). 
To  apply, follow the link 
https://candidatures-calliope.polytechnique.fr/calliope-fo/recherche/index.php?lang=en
The position  number is 71.

Inferring causation from time series with perspectives in Earth system sciences – Jakob Runge

The seminar is on December 4th at 10:00 14:00 and will be held remotely.

Link to the zoom session: https://us02web.zoom.us/j/84003686532

Jakob Runge presentation is entitled:

« Inferring causation from time series with perspectives in Earth system sciences »

Abstract:

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In disciplines dealing with complex dynamical systems, such as the Earth system, replicated real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal inference methods beyond the commonly adopted correlation techniques. In this talk, I will present a recent Perspective Paper in Nature Communications giving an overview of causal inference methods and identify key tasks and major challenges where causal methods have the potential to advance the state-of-the-art in Earth system sciences. Several methods will be illustrated by `success’ examples where causal inference methods have already led to novel insights and I will close with an outlook of this relatively new and exciting field. I will also present the causal inference benchmark platform www.causeme.net that aims to assess the performance of causal inference methods and to help practitioners choose the right method for a particular problem.

Runge, J., S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, M. Kretschmer, M. D. Mahecha, J. Muñoz-Marı́, E. H. van Nes, J. Peters, R. Quax, M. Reichstein, M. Scheffer, B. Schölkopf, P. Spirtes, G. Sugihara, J. Sun, K. Zhang, and J. Zscheischler (2019). Inferring causation from time series in earth system sciences. Nature Communications 10 (1), 2553.

Short bio:

Jakob Runge heads the Climate Informatics working group at the German Aerospace Center’s Institute of Data Science since 2017. The group combines innovative data science methods from different fields (graphical models, causal inference, nonlinear dynamics, deep learning) and closely works with experts in the climate sciences. Jakob studied physics at Humboldt University Berlin and obtained his PhD at the Potsdam Institute for Climate Impact Research in 2014. For his studies, he was funded by the German National Foundation (Studienstiftung) and his thesis was awarded the Carl-Ramsauer prize by the Berlin Physical Society. In 2014 he won a $200.000 Fellowship Award in Studying Complex Systems by the James S. McDonnell Foundation and joined the Grantham Institute, Imperial College, from 2016 to 2017. On https://github.com/jakobrunge/tigramite.git he provides Tigramite, a time series analysis python module for causal inference. For more details, see: www.climateinformaticslab.com

A direct approach to detection and attribution of climate change – Eniko Szekely – 24/01/2020

Lien pour les slides

Le prochain séminaire aura lieu le 24 Janvier à 14h30 au campus Pierre et Marie Curie de Sorbonne Université dans la salle 105 du LIP6 couloir 25-26 au 1er étage.

La présentation de Eniko Szekely est intitulée:

« A direct approach to detection and attribution of climate change »

Abstract:

In this talk I will present a novel statistical learning approach for detection and attribution (D&A) of climate change. Traditional optimal D&A studies try to directly model the observations from model simulations, but practically this is challenging due to high-dimensionality. Here, we propose a supervised approach where we predict a given metric or external forcing directly from the high-dimensional spatial pattern of climate variables, and use the predicted metric as a test statistic for D&A. The first part of the talk will focus on daily detection and show that we can now detect climate change from global weather for any single day since spring 2012. The second part of the talk will focus on attribution of climate change. For attribution, we want the prediction of the external forcing, e.g., anthropogenic forcing, to work well even under changes in the distribution of other external forcings, e.g., solar or volcanic forcings. Therefore we formulate the optimization problem from a distributional robustness perspective, and use anchor regression to ensure good predictions even under such distributional changes.

Notice Biographie:

Eniko is a senior data scientist at the Swiss Data Science Center, EPFL & ETH Zurich, working on machine learning for climate science. Previously, she was a postdoctoral researcher at the Courant Institute of Mathematical Sciences, New York University, and she obtained her PhD in Computer Science from the University of Geneva, Switzerland. Broadly she is interested in machine learning for high-dimensional data and nonlinear phenomena arising from dynamical systems. More recently she has been working on using machine learning and statistical learning approaches for climate science, and has been involved in the organization of the Climate Informatics workshop since 2015.

Groupe de travail 1 : Evangelos Moscos

Quand : 4 Décembre 2019 à 14:00

Où : Salle de réunion du LOCEAN, Tour 45-55, 4ème étage

Evangelos Moscos (LMD,LIP6) présentera sa problématique de recherche qui porte sur « Identification de structures tourbillonnaires dans la Méditerranée par Deep Learning ». Le sujet aborde des problèmes méthodologiques liés à la détection de structures à différentes échelles qui se retrouvent dans de nombreux problèmes.

 L’exposé sera suivi d’une discussion avec les participants sur l’approche et les perspectives possibles du travail.

 Le groupe de travail interne « SCAI & AI4Climate » réunit les chercheurs, ingénieurs, doctorants, post doctorants concernés par les thématiques liées à conception et l’utilisation de nouvelles méthodes d’Intelligence Artificielle pour l’étude de l’environnement, allant du modèle à l’observation. Les premières réunions seront consacrées aux travaux des doctorants qui commencent leur thèse cette année dans ce cadre.

Deep Learning for Satellite Imagery: Semantic Segmentation, Non-Rigid Alignment, and Self-Denoising – Guillaume Charpiat – 4 Decembre 2019

Quand : 4 Decembre 2019 à 10:30

Où : Campus Pierre and Marie Curie (Sorbonne Université) salle 105 du LIP6 couloir 25-26 1er étage.

Résumé
Neural networks have been producing impressive results in computer vision these last years, in image classification or segmentation in particular. To be transferred to remote sensing, this tool needs adaptation to its specifics: large images, many small objects per image, keeping high-resolution output, unreliable ground truth (usually mis-registered). We will review the work done in our group for remote sensing semantic segmentation, explaining the evolution of our neural net architecture design to face these challenges, and finally training a network to register binary cadaster maps to RGB images while detecting new buildings if any, in a multi-scale approach. We will show in particular that it is possible to train on noisy datasets, and to make predictions at an accuracy much better than the variance of the original noise. To explain this phenomenon, we build theoretical tools to express input similarity from the neural network point of view, and use them to quantify data redundancy and associated expected denoising effects.
If time permits, we might also present work on hurricane track forecast from reanalysis data (2-3D coverage of the Earth’s surface with temperature/pressure/etc. fields) using deep learning.

Notice Biographie:

After a PhD thesis at ENS on shape statistics for image segmentation, and a year in Bernhard Schölkopf’s team at MPI Tübingen on kernel methods for medical imaging, Guillaume Charpiat joined INRIA Sophia-Antipolis to work on computer vision, and later INRIA Saclay to work on machine learning. Lately, he has been focusing on deep learning, with in particular remote sensing imagery as an application field.

Affiliation:
Guillaume Charpiat (Équipe TAU, INRIA Saclay / LRI – Université Paris-Sud)