Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations

4 September 2020

Papers citing "Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations"

10 / 10 papers shown

Title
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization Marc Bocquet J. Brajard A. Carrassi Laurent Bertino 54 104 0 17 Jan 2020
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model J. Brajard A. Carrassi Marc Bocquet Laurent Bertino 36 225 0 06 Jan 2020
Learning Dynamical Systems from Partial Observations Ibrahim Ayed Emmanuel de Bézenac Arthur Pajot J. Brajard Patrick Gallinari AI4TS 59 91 0 26 Feb 2019
Data Driven Governing Equations Approximation Using Deep Neural Networks Tong Qin Kailiang Wu D. Xiu PINN 71 273 0 13 Nov 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 414 5,111 0 19 Jun 2018
Multi-Step Prediction of Dynamic Systems with Recurrent Neural Networks Nima Mohajerin Steven L. Waslander AI4CE 78 90 0 20 May 2018
Deep learning algorithm for data-driven simulation of noisy dynamical system K. Yeo Igor Melnyk AI4TS 63 94 0 22 Feb 2018
Filtering Variational Objectives Chris J. Maddison Dieterich Lawson George Tucker N. Heess Mohammad Norouzi A. Mnih Arnaud Doucet Yee Whye Teh FedML 232 210 0 25 May 2017
Sequential Neural Models with Stochastic Layers Marco Fraccaro Søren Kaae Sønderby Ulrich Paquet Ole Winther BDL 112 398 0 24 May 2016
Importance Weighted Autoencoders Yuri Burda Roger C. Grosse Ruslan Salakhutdinov BDL 268 1,245 0 01 Sep 2015