Data-driven simulation for general purpose multibody dynamics using deep neural networks

This is because ML is effective to handle and interpret big data sets for the purpose of finding certain patterns from the data. In particular, Deep Neural Network (DNN), which is based on an Artificial Neural Network (ANN) with multiple hidden layers between input and output layers allows to handle complex shapes with nonlinear functions with multidimensional input data. DNN has been successfully used in a large number of practical applications. Well trained neural network then provides precise pattern recognition based on data sets in real time. These features, big data recognition and real time estimation of nonlinear functions, of ML approaches are attractive to dynamics and control engineers who are handling nonlinear system dynamics with real world data. There have been several previous studies on applying ML, DNN, or other big-data handling techniques to rigid multibody system problems.