Latent assimilation with implicit neural representations for unknown dynamics

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Parabolic equations and parabolic systems (35Kxx)

Cites Work

Unnamed Item
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Strong and weak constraint variational assimilations for reduced order fluid flow modeling
Reservoir computing approaches to recurrent neural network training
Spherical harmonics and approximations on the unit sphere. An introduction
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
Reduced-order modeling of the upper tropical Pacific ocean model using proper orthogonal decomposition
The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
Optimal nonlinear approximation
Wavelet transform adapted to an approximate Kalman filter system
DGM: a deep learning algorithm for solving partial differential equations
Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models
A comparison of neural network architectures for data-driven reduced-order modeling
Optimal reduced space for variational data assimilation
Machine learning for fast and reliable solution of time-dependent differential equations
PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Dynamic mode decomposition of numerical and experimental data
Turbulence and the dynamics of coherent structures. I. Coherent structures
An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error
Chapter 1: Introduction to data assimilation and inverse problems
Machine Learning for Data Science Handbook

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