1 citations · 1 across the 6 of their papers we have counts for
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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…
Invertible continuous latent dynamic for long-term data assimilation in complex physical systems
Rares Grozavescu, Pengyu Zhang, Etienne Meunier +1
Forward forecasting and data assimilation are the two important aspects in physical simulation: one propagates the state forward, the other recovers unknown states from sparse obse…
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…
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…