Search Results for author:
Found 9 papers, 2 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.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
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.
Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations
The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest.
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.
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
To solve for the joint inference of the hidden dynamics and of model parameters, we combine neural-network representations and state-of-the-art assimilation schemes.
Sea surface temperature prediction and reconstruction using patch-level neural network representations
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges.
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches.
