Categorizing Simulation Models Using Convolutional Neural Networks

Engineers come across frequent problems solved using artificial intelligence. Tasks of neural networks are often considered solving problems in such an intelligent manner. Whether as an optimization problem or development tool, neural networks help engineers and create a more efficient approach. Furthermore, information is stored in a unique data structure, during a project cycle. Engineers often face a time-consuming search to get an understanding of the project itself. This process of understanding the data needs to be repeated by every engineer, if not further documented. E.g. searching for a past project with similar parameters can be a big task, regarding huge databases. Worker fluctuation can increase the cost of this process as well. To address this issue, neural networks make a significant contribution. The main aspect of this paper is to add meta data to the core files of the project. With such labels it is possible to get access to the content of the model files of an engine performance simulation tool without opening them. At first a pre-processing approach and its design is introduced to extract and filter the necessary data from an XML data structure. Then, the data is split into sequences. In addition, a convolutional neural network design is examined to add labels, characterizing the simulation architecture, as meta data to these sequences.