6 papers
Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting
Rares Grozavescu, Pengyu Zhang, Etienne Meunier +1
Forecasting physical systems over long horizons from irregularly sampled observations demands models that are stable, computationally efficient, and free of fixed-timestep assumpti…
Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach
Rares Grozavescu, Pengyu Zhang, Mark Girolami +1
We investigate the Continuous-Time Koopman Autoencoder (CT-KAE) as a lightweight surrogate model for long-horizon ocean state forecasting in a two-layer quasi-geostrophic (QG) syst…
Jacobian Regularization Stabilizes Long-Term Integration of Neural Differential Equations
Maya Janvier, Julien Salomon, Etienne Meunier
Hybrid models and Neural Differential Equations (NDE) are getting increasingly important for the modeling of physical systems, however they often encounter stability and accuracy i…
Towards fully differentiable neural ocean model with Veros
Etienne Meunier, Said Ouala, Hugo Frezat +2
We present a differentiable extension of the VEROS ocean model, enabling automatic differentiation through its dynamical core. We describe the key modifications required to make th…
A Framework for Hybrid Physics-AI Coupled Ocean Models
Laure Zanna, William Gregory, Pavel Perezhogin +23
Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Par…
Learning to generate physical ocean states: Towards hybrid climate modeling
Etienne Meunier, David Kamm, Guillaume Gachon +2
Ocean General Circulation Models require extensive computational resources to reach equilibrium states, while deep learning emulators, despite offering fast predictions, lack the p…