The Future of Engineering Crash Testing With BMW Group | AI for Automotive
Automotive Crash Test Case Study With BMW
This webinar will showcase how Monolith’s self-learning models can benefit test engineers in the automotive industry.
This webinar focuses primarily on cost and time-critical, as well as safety-critical crash test applications, and how predictive, data-driven models are cutting down testing time and costs, whilst preserving the integrity and results without disrupting existing engineering workflows.
Who should watch?
What we will cover:
- How the engineers at BMW Group used the Monolith software to predict vehicle performance before design or testing has begun, whilst dramatically accelerating product development.
- How Monolith’s self-learning models are instantly predicting the performance of highly complex systems like crash tests.
- How the Monolith platform leverages valuable engineering data and machine learning to rapidly find critical new insights hidden in historic and current test data.
Monolith leverages the power of self-learning models embedded in a no-code cloud environment to yield:
Faster test cycles and performance insights
- A full exploration of multiple virtual test scenarios in one place
- Reduced costs and time investment for testing phases
- Accelerated engineering R&D
- Solve intractable physical problems
Meet our speakers
Oliver J. Walter
Oliver is an automotive industry expert, with over 20 years of experience in the field. Most notably, Oliver spent the bulk of his career managing different projects within BMW Group, and is now a consultant working closely with Monolith.
Jousef is responsible for product marketing at Monolith. He studied mechanical engineering at the Karlsruhe Institute of Technology (KIT) where he focused on computational mechanics, turbulence modeling & AI.