Time-Variant Digital Twin Modeling through the Kalman-Generalized Sparse Identification of Nonlinear Dynamics

Author:

Wang Jingyi1,Moreira Jesus2,Cao Yankai1,Gopaluni Bhushan1

Affiliation:

1. University of British Columbia,Department of Chemical and Biological Engineering,Vancouver,BC,Canada,V6T 1Z3

2. Imperial Oil Company,Calgary,AB,Canada,T2C 5N1

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

IEEE

Reference21 articles.

1. Data driven modeling based on dynamic parsimonious fuzzy neural network

2. Discovering governing equations from data by sparse identification of nonlinear dynamical systems;brunton;Proceedings of the National Academy of Sciences,2016

3. Sparsity Promoting H₂-Conic Control

4. Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

5. Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data;kaheman,2020

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