The future success of the automotive OEM will closely be related to the adaption of artificial intelligence (AI) in all its domains, e.g., in the engineering process, in production, and in the software-defined vehicle itself. In more detail, fast AI surrogate models can speed up time-consuming simulations in the engineering process by a huge factor – independent of the simulation domain, being it fluid, structural, thermal, or electrical. Hence, AI surrogate models can be used to identify the optimal configuration, something that is often practically impossible using traditional simulations. Moreover, the automotive OEM owns huge datasets, originating for example from the development and testing, from production or from the fleet. To gain knowledge from these datasets can be of upmost importance. One key is the data-driven detection of unexpected data using AI, for example, for predictive maintenance or for AI-driven data correction. In addition, one can use data to predict its future evolution to predict, e.g., driving profiles or the power consumption of the vehicle electrical system. In this talk, we introduce the AI framework OPTIMALIS® , demonstrate its broad range of application and highlight its intuitive usage. The key advantages of OPTIMALIS® are its standardized data-interface, its ability to automatically adopt to data, and its autonomy. As such, OPTIMALIS® is the ideal tool to test the usage of AI in your use-case and to leverage your business to the next level.
Session:
Poster
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