You can find here all the informations and presentations of interest from Friday morning OceaniX team meetings.
Zoom conference for session on Jan. 15: link
Space oceanography missions, especially altimeter missions, have considerably improved the observation of sea surface dynamics over the last decades. They can however hardly resolve spatial scales below ∼ 100km. Meanwhile the AIS (Automatic Identification System) monitoring of the maritime traffic implicitly conveys information on the underlying sea surface currents as the trajectory of ships is affected by the current. Here, we show that an unsupervised variational learning scheme provides new means to elucidate how AIS data streams can be converted into sea surface currents. The proposed scheme relies on a learnable variational framework and relate to variational auto-encoder approach coupled with neural ODE (Ordinary Differential Equation) solving the targeted ill-posed inverse problem. Through numerical experiments on a real AIS dataset, we demonstrate how the proposed scheme could contribute to the reconstruction of sea surface currents from AIS data.
The presence of pattern of inter-connections between nodes (called network motifs) in real complex networks has been widely studied and documented. Our study aims to search for meaningful patterns in a MLP network before and after training. Simulations show that the final network topology is shaped by learning dynamics, but can be strongly biased by choosing appropriate weight initialization schemes. Overall, our results suggest that non-trivial initialization strategies can make learning more effective by promoting the development of useful network motifs, which are often surprisingly consistent with those observed in general transduction networks.
Tutorial on PyTorch package for SDEs
Nowadays, the multi-scale character of complex natural systems is confirmed as a fact. Turbulence, which is considered as the last frontier of classical physics, is used as a benchmark of this type of systems. Thus due to its tremendous importance, several frameworks have been historically developed to describe it. However, the existing approaches present limitations to cover the full description of multi-scale couplings. As a consequence of these drawbacks, new progresses in the field are needed. Thus, I propose to develop a statistical framework based on Information Theory to deal with multi-scale coupling descriptions of physical systems and processes.
Short tutorial on a framework for dataset control and versioning