SimExplore uses state-of-the-art machine learning methods and applies them to the analysis of CAE data. The SimExplore workflow consists of two steps, with an offline batch process followed by interactive exploration and analysis. The approach is available as a standalone tool and designed for integration with other software clients.
An exploration of many simulations and a fast 3D preview becomes possible with our dimensionality reduction methodology. Such a representation helps in clustering many simulations and detecting outliers, this leads to a significant speed-up in CAE data analysis.
© Photo Fraunhofer IAIS
Benefit & value
In SimExplore, current machine learning methods are applied to large amounts of simulation data to identify commonalities and differences between the various simulations, making post-processing much faster and clearer. The typical process consists of two steps: after a batch process, in which the necessary calculations such as a dimension reduction are performed, an interactive visualization of the different simulations is available. Specifically, SimExplore automatically detects simulations or outliers that behave similarly. SimExplore can be used as a stand-alone tool or integrated into existing SDM software solutions.
SimExplore supports CAE engineers in product development and makes their daily work much easier.