  This paper explores the prediction of the dynamics of piecewise smooth maps
using various deep learning models. We have shown various novel ways of
predicting the dynamics of piecewise smooth maps using deep learning models.
Moreover, we have used machine learning models such as Decision Tree
Classifier, Logistic Regression, K-Nearest Neighbor, Random Forest, and Support
Vector Machine for predicting the border collision bifurcation in the 1D normal
form map and the 1D tent map. Further, we classified the regular and chaotic
behaviour of the 1D tent map and the 2D Lozi map using deep learning models
like Convolutional Neural Network (CNN), ResNet50, and ConvLSTM via cobweb
diagram and phase portraits. We also classified the chaotic and hyperchaotic
behaviour of the 3D piecewise smooth map using deep learning models such as the
Feed Forward Neural Network (FNN), Long Short-Term Memory (LSTM), and Recurrent
Neural Network (RNN). Finally, deep learning models such as Long Short-Term
Memory (LSTM) and Recurrent Neural Network (RNN) are used for reconstructing
the two parametric charts of 2D border collision bifurcation normal form map.
