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About Accepted Manuscript: Aerospace Science and Technology
The document describes a new physics-based learning method for simulating aircraft dynamics. The method uses a deep residual recurrent neural network (DR-RNN) that integrates the underlying physics of aircraft dynamics through an implicit integration scheme. The method is demonstrated on simulating the dynamics of a Boeing 747 aircraft under different controls and disturbances. The prediction accuracy and training cost of the physics-based learning method are compared to a purely data-driven long short-term memory (LSTM) network, showing that incorporating physics knowledge improves performance while reducing training costs compared to non-physics based methods. The computational efficiency of the new method is also found to be better than classical numerical methods for simulating aircraft dynamics.
Detailed Information
| Author: | ['Najah Nurhasanah'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 26 |
| Language: | English |
| Format: | |
| Price: | FREE |
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