Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics

no code implementations • 3 Nov 2023 • Maxime Beauchamp, Ronan Fablet, Hugo Georgenthum

Recent advancements in deep learning also addressed this issue by incorporating data assimilation into neural architectures: it treats the reconstruction task as a joint learning problem involving both prior model and solver as neural networks.

Gaussian Processes Uncertainty Quantification

Learning Neural Optimal Interpolation Models and Solvers

no code implementations • 14 Nov 2022 • Maxime Beauchamp, Joseph Thompson, Hugo Georgenthum, Quentin Febvre, Ronan Fablet

The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations.

Earth Observation Gaussian Processes