Summary of Machine Learning Based Prediction Of Ditching Loads, by Henning Schwarz et al.
Machine Learning based Prediction of Ditching Loads
by Henning Schwarz, Micha Überrück, Jens-Peter M. Zemke, Thomas Rung
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents machine learning approaches to predict dynamic ditching loads on aircraft fuselages. The methods involve reconstructing spatial loads using convolutional autoencoders (CAEs) and predicting transient behavior using long short-term memory (LSTM) networks or Koopman-operator based methods. The training data is generated using an extension of the momentum method, and the approach is applied to a full-scale fuselage of a DLR-D150 aircraft. Results show satisfactory predictive agreement for all four investigated surrogate models, with the combination of LSTM and deep decoder CAE showing the best performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses machine learning to predict how much force an airplane’s body will experience during landing. It breaks this problem into two parts: first, it uses a special kind of neural network called a convolutional autoencoder to figure out where the forces are on the plane’s body. Then, it uses another type of neural network, either long short-term memory or Koopman-operator based, to predict how these forces change over time. The data used to train these models is generated using a special formula. The researchers tested their approach on a real airplane and found that all four models were good at predicting the forces, with one combination being the best. |
Keywords
* Artificial intelligence * Autoencoder * Decoder * Lstm * Machine learning * Neural network
