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Found 11 papers, 4 papers with code
Online Calibration of Deep Learning Sub-Models for Hybrid Numerical Modeling Systems
Most of these efforts in defining hybrid dynamical systems follow {offline} learning strategies in which the neural parameterization (called here sub-model) is trained to output an ideal correction.
Inversion of sea surface currents from satellite-derived SST-SSH synergies with 4DVarNets
This is however limited to the surface-constrained geostrophic component of sea surface velocities.
Guided Unsupervised Learning by Subaperture Decomposition for Ocean SAR Image Retrieval
Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications.
Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies
We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations.
Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery
Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.
Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields
For numerous earth observation applications, one may benefit from various satellite sensors to address the reconstruction of some process or information of interest.
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning
The complexity of real-world geophysical systems is often compounded by the fact that the observed measurements depend on hidden variables.
Learning Runge-Kutta Integration Schemes for ODE Simulation and Identification
Inevitably, a numerical simulation of a differential equation will then always be distinct from a true analytical solution.
The speed of breaking waves controls sea surface drag
Originally suggested on purely dimensional grounds, this roughness length does not directly correspond to a measurable physical quantity of the wind-and-wave system.
Fluid Dynamics Atmospheric and Oceanic Physics
Learning Variational Data Assimilation Models and Solvers
Intriguingly, we also show that the variational models issued from the true Lorenz-63 and Lorenz-96 ODE representations may not lead to the best reconstruction performance.
Learning Latent Dynamics for Partially-Observed Chaotic Systems
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i. e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns.
