8 papers
Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +3
Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enabling strong performance in long…
ADDA: a Modular Framework for Representing, Simulating and Assimilating Dynamics with End-to-end Differentiability
Anthony Frion, Vien Minh Nguyen-Thanh, Ali Can Bekar +3
Data assimilation (DA) is an essential tool for prediction and understanding in the geosciences. DA combines simulation programs representing scientific knowledge with observations…
Uncertainty-aware data assimilation through variational inference
Anthony Frion, David S Greenberg
Data assimilation, consisting in the combination of a dynamical model with a set of noisy and incomplete observations in order to infer the state of a system over time, involves un…
Augmented Invertible Koopman Autoencoder for long-term time series forecasting
Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2
Following the introduction of Dynamic Mode Decomposition and its numerous extensions, many neural autoencoder-based implementations of the Koopman operator have recently been propo…
Koopman Ensembles for Probabilistic Time Series Forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +2
In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently b…
Neural Koopman prior for data assimilation
Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2
With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data…
Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +2
Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth's surface have been made publicly available for scientific purpose,…
Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data
Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2
Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying phys…