Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields

International audienceWind speed at sea surface is a key quantity for a variety of scientific applications and human activities. Due to the non-linearity of the phenomenon, a complete description of such variable is made infeasible on both the small scale and large spatial extents. Methods relying on Data Assimilation techniques, despite being the state-of-the-art for Numerical Weather Prediction, can not provide the reconstructions with a spatial resolution that can compete with satellite imagery. In this work we propose a framework based on Variational Data Assimilation and Deep Learning concepts. This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed. We design our experiments using synthetic wind data and different sampling schemes for high-resolution and low-resolution versions of original data to emulate the real-world scenario of spatio-temporally heterogeneous observations. Extensive numerical experiments are performed to assess systematically the impact of low and high-resolution wind fields and in-situ observations on the model reconstruction performance. We show that in-situ observations with richer temporal resolution represent an added value in terms of the model reconstruction performance. We show how a multi-modal approach, that explicitly informs the model about the heterogeneity of the available observations, can improve the reconstruction task by exploiting the complementary information in spatial and local point-wise data. To conclude, we propose an analysis to test the robustness of the chosen framework against phase delay and amplitude biases in low-resolution data and against interruptions of in-situ observations supply at evaluation tim

Application du chiffrement fonctionnel sur données confidentielles pour la conception de modèles d'apprentissage automatique

International audienceL'accès aux données est un prérequis à la conception de modèle par apprentissage automatique. Dans certains secteurs d'application, comme la santé, le bancaire ou la défense, les données sont jugées confidentielles si bien que leur partage est particulièrement complexe, voire impossible. En réponse à ce verrou applicatif, nous nous intéressons dans ces travaux au chiffrement fonctionnel, technique de cryptographie qui permet l'accès sécurisé à fonction spécifique de données chiffrées. En nous appuyant sur un cas d'application concret de l'industrie de défense, nous montrons que le chiffrement fonctionnel est d'ores et déjà applicable à des données représentatives de la réalité industrielle et étudions son impact sur les performances de modèles obtenus par apprentissage automatique

Trainable dynamical estimation of above-surface wind speed using underwater passive acoustics

International audienceCovering more than 70% of Earth surface, oceans play a key role in climate regulation, are the main medium of world commercial trade and are a source of renewable energy, to cite few aspects. Despite its importance, ocean surface state reconstruction poses some challenges, due to its non-linear behavior and the heterogeneity of the spatio-temporal scales involved. State-of-the-art techniques for forecast and prediction involve numerical weather models, such as data assimilation approaches. Besides, remote sensing techniques deliver finer-grained information about the surface state. Among others, underwater passive acoustics uses the underwater soundscape to infer the above-surface atmospheric state. In this work, with a particular focus on the surface wind speed reconstruction, we propose a framework that bridges data assimilation and machine learning schemes, to exploit both the prior physical knowledge and the capability of machine learning modelling to take advantage of large data bases. Extensive numerical experiments show that this hybrid framework can outperform the state-of-the-art data-driven models with a relative gain up to 16% in terms of root mean squared error. Experiments also involve tests on multi-modal data, namely underwater passive acoustics and wind speed reanalyses, giving promising results