Artificial neural networks (ANNs) are often used for short term discrete time series predictions. Continuous-time models are, however, required for qualitatively correct approximations to long-term dynamics (attractors) of nonlinear dynamical systems and their transitions (bifurcations) as system parameters are varied. In previous work the authors developed a black-box methodology for the characterization of experimental time series as continuous-time models (sets of ordinary differential equations) based on a neural network platform. This methodology naturally lends itself to the identification of partially known first principles dynamic models, and here the authors present its extension to "gray-box" identification

Machine Learning / AI

Paper

https://ieeexplore.ieee.org/document/366006

Machine Learning / AI

Paper

https://pubs.rsc.org/en/content/articlelanding/2021/na/d0na00600a

Machine Learning / AI

Paper

https://www.nature.com/articles/s42254-021-00314-5

Engineering, Other

Machine Learning / AI

Paper

https://www.computer.org/csdl/proceedings-article/fpl/2021/375900a024/1xDQ3iK1FRK

Engineering, Other

Machine Learning / AI

Paper

https://dl.acm.org/doi/10.1145/3431920.3439283

Engineering, Other

Machine Learning / AI

Paper

https://dl.acm.org/doi/10.1145/3431920.3439296

Engineering, Other

Machine Learning / AI

Paper

https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8952724