The spin-off Renumics GmbH uses machine learning methods to make Computer Aided Engineering more efficient and take the strain off computational engineers.
Crash tests are an expensive affair. In early development stages, collision experiments are therefore often replaced by computer simulations that can be performed thousands of times taking various influential factors into account. These simulations are usually based on computer-supported processes, on so-called Computer Aided Engineering (CAE). This concept centres on computational engineers who compile numerical models, thus assisting constructors in the analysis and optimisation of their designs. The crucial time and cost factors here are the many manual work steps involved. For example, computational engineers invest a considerable amount of time in routine activities such as pre-processing geometries and integrating data instead of being able to concentrate on modelling and analytical work, which is precisely where Renumics comes in. This KIT spin-off has developed a software with which CAE can be automated. In this context, machine learning methods help make simulations workflows considerably more efficient.