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Title: Data driven forecasting of aperiodic motions of non-autonomous systems
In the present effort, a data-driven modeling approach is undertaken to forecast aperiodic responses of non-autonomous systems. As a representative non-autonomous system, a harmonically forced Duffing oscillator is considered. Along with it, an experimental prototype of a Duffing oscillator is studied. Data corresponding to chaotic motions are obtained through simulations of forced oscillators with hardening and softening characteristics and experiments with a bistable oscillator. Portions of these datasets are used to train a neural machine and make response predictions and forecasts for motions on the corresponding attractors. The neural machine is constructed by using a deep recurrent neural network architecture. The experiments conducted with the different numerical and experimental chaotic time-series data confirm the effectiveness of the constructed neural network for the forecasting of non-autonomous system responses.  more » « less
Award ID(s):
1854532
PAR ID:
10232187
Author(s) / Creator(s):
Date Published:
Journal Name:
Chaos
Volume:
31
ISSN:
2646-6783
Page Range / eLocation ID:
021105-1
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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