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.
The Renumics founding team (left to right): Steffen Slavetinsky, Markus Stoll and Dr Stefan Suwelack.
The company Lightrig GmbH develops a software that allows its users to manipulate the lighting of virtual objects in visualisation and animation by means of comfortable inputs. It is used especially in film, TV and media production. Not only does it facilitate the consistent, credible lighting of virtual objects, it also helps implement design specifications in a harmonious manner. We have interviewed the Lightrig team on their idea, the foundation of their company and their future perspective.
An example visualisation of light transport using Lightrig (source: Lightrig)
The company Falquez, Pantle und Pritz GbR offers software-as-a-service for flow and noise prognoses in technical device development. With the help of the cloud-based platform NUBERISIM, complex high performance simulations can be driven and operated easily via browser. Therefore, potential sources of noise are found as early as product development and measures for noise minimization can be taken early on. In an interview, we asked Dr. Iris Pantle about the idea, the startup time, and the future prospects.
Team of NUBERISIM (f.l.): Balazs Pritz, Iris Pantle, Carlos Falquez (Source: KIT / Markus Breig)