Summary of Deep Learning For Prediction and Classifying the Dynamical Behaviour Of Piecewise Smooth Maps, by Vismaya V S et al.
Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps
by Vismaya V S, Bharath V Nair, Sishu Shankar Muni
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Chaotic Dynamics (nlin.CD)
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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 A novel approach to predicting the dynamics of piecewise smooth maps is presented, utilizing various deep learning models. The study demonstrates the effectiveness of decision trees, logistic regression, k-nearest neighbors, random forests, and support vector machines in forecasting the border collision bifurcation in 1D normal form maps and tent maps. Additionally, convolutional neural networks (CNN), ResNet50, and ConvLSTM are employed to classify regular and chaotic behavior in 1D tent maps and 2D Lozi maps via cobweb diagrams and phase portraits. Furthermore, feed forward neural networks (FNN), long short-term memory (LSTM) recurrent neural networks (RNN) are used for reconstructing the two parametric charts of 2D border collision bifurcation normal form map. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special kinds of computer programs called “deep learning models” to figure out how different things work. They use these programs to look at maps that have different parts with different rules, and they try to predict what will happen next. They tested some common methods like decision trees and logistic regression, but also used more complicated ones like convolutional neural networks and recurrent neural networks. The results show that these deep learning models can help us understand and classify the behavior of these maps in different ways. |
Keywords
* Artificial intelligence * Cnn * Deep learning * Logistic regression * Lstm * Rnn
