Catégorie : Working Groups

The SCAI / SAMA Workshops are focused on a specific scientific question, usually inspired by the work of a SCAI-Geosciences PhD student, but potentially by an intern’s or researcher’s work. They vary in frequency, usually one is heald every three months. They are comprised of an introductory talk by the presenter, followed by a monitored discussion.

Working group 4: Marie Dechelle – Bridging Dynamical Models and Deep Networks to Solve Forward and Inverse Problems

 21 Juin à 10h
Campus de Jussieu, Salle de réunion SCAI,
Bâtiment Esclangon 1er étage
Zoom: https://zoom.us/j/98265750503

Partially observed dynamical systems embrace a wide class of phenomena and represent an overwhelming majority of Earth science modeling, traditionally relying on ordinary or partial differential equations. Recent trends consider Machine Learning as an alternative or complementary approach to traditional physical models, allowing the integration of observations and potentially faster computations through model reduction. In this regard, latest works study the learning of the decomposition between model-based (MB) and data driven (ML) dynamical representations. However, learning such a decomposition with the sole supervision on the trajectories is ill-posed.

We introduce a learning algorithm to bridge model-based prediction and data-based algorithms, while solving the ill-posedness. This one relies on a cost function based on the computation of an upper bound of the prediction error, which enables us to minimize the contribution of the data driven algorithm while recovering physical parameters of the MB part. We evidence the soundness of our approach on a physical dataset based on simplified Navier-Stokes equations. We also present preliminary results on outputs of the ocean model NATL60.

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


Working group 3: Pierre Lepetit – Estimation of visibility and snow height on webcam images with learning to rank approach

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

16 Mars à 10h
sur le campus de Jussieu,
Salle de réunion SCAI
Batiment Esclangon 1er étage

Participer à la réunion Zoom
(voir information de connexion ci-dessous)

  • The image-based estimation of meteorological parameters provides clear benefits for surface weather observation. When a local event arises, as a dense fog or a snow settling, webcams and CCTV cameras are sources of valuable information. These images actually inform about the class of weather (sunny, rainy, foggy, snowy, etc). They also enable to gauge quantitative parameters as the horizontal visibility (the farest you can see), the snow height, the precipitation rate, etc, with a variable precision.
  • Recently, the weather classification task has been successfully addressed by deep learning approaches. However, the quantitative estimation faces a strong difficulty: the existing data sets that contain both images and precise weather measurements are rare and involve only few different outdoor scenes. It is virtually impossible for an expert to assign image-wise quantitative labels, but it is possible to compare two images from the same webcam and therefore assign pairwise labels. An “uncomparable” label being assigned to couples for which the expert is not able to distinguish the two images with respect to the parameter.
  • This analysis gives the starting point of the workshop. The discussion will deal with the methods of labeling, learning to rank and calibration that may help to yield such comparisons and to predict ordinal or quantitative estimations of visibility and snow height. The way uncomparable pairs could lead to predict an image-wise uncertainty will also be addressed.

Participer à la réunion Zoom

https://zoom.us/j/98278319724

ID de réunion : 982 7831 9724

Trouvez votre numéro local : https://zoom.us/u/agSnuNJYM

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.

The a similar talk can be found here, and an early conference paper can be found here.

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.