13 papers
Optimal sensor placement for the reconstruction of ocean states using differentiable Gumbel-Softmax sampling operator
Oscar Chapron, Ronan Fablet, Yann Stéphan
Accurately reconstructing and forecasting ocean fields from sparse observations is critical for both operational and scientific purposes. Optimizing sensor placement to maximize re…
Majorization-Minimization Networks for Inverse Problems: An Application to EEG Imaging
Le Minh Triet Tran, Sarah Reynaud, Ronan Fablet +3
Inverse problems are often ill-posed and require optimization schemes with strong stability and convergence guarantees. While learning-based approaches such as deep unrolling and m…
Take It or Leave It: Intent-Controlled Partial Optimal Transport
Salil Parth Tripathi, Bertrand Chapron, Fabrice Collard +2
While optimal transport (OT) enforces a rigid constraint by requiring two measures to be matched exactly, partial optimal transport relaxes this requirement by allowing mass to rem…
Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations
Daria Botvynko, Carlos Granero-Belinchon, Simon Van Gennip +2
We assess the influence of different Eulerian geophysical input fields on Lagrangian drift simulations using DriftNet, a learning-based method designed to simulate Lagrangian drift…
Static and auto-regressive neural emulation of phytoplankton biomass dynamics from physical predictors in the global ocean
Mahima Lakra, Ronan Fablet, Lucas Drumetz +2
Phytoplankton is the basis of marine food webs, driving both ecological processes and global biogeochemical cycles. Despite their ecological and climatic significance, accurately s…
Discovering Data Manifold Geometry via Non-Contracting Flows
David Vigouroux, Lucas Drumetz, Ronan Fablet +1
We introduce an unsupervised approach for constructing a global reference system by learning, in the ambient space, vector fields that span the tangent spaces of an unknown data ma…