Pioneering AI technology software solves previously impossible physics challenges in vehicle performance
Monolith, an AI software platform has announced that engineering teams at the BMW Group are using its software to accelerate the development of its world-class vehicles. By training Monolith self-learning models with the company’s valuable engineering test data, engineers can now use AI to solve previously impossible physics challenges and instantly predict the performance of highly complex systems like crash and aerodynamics tests.
The BMW Group crash test engineering team began working with Monolith in 2019 via the BMW Startup Garage to explore the potential of using AI to predict the force on a passenger’s tibia during a crash. Current crash development uses 1000s of simulations as well as physical tests to capture performance. Even with sophisticated modelling, owing to the complexity of the physics underpinning crash dynamics, results require substantial engineering know-how to calibrate for real world behaviour. Moreover, physical crash tests can only be conducted in later stages of development when the design is mature enough to create physical prototypes. Exploring a more efficient solution, the BMW Group collaborated with Monolith to see if AI could predict crash performance and importantly, substantially earlier in the vehicle development process. The BMW Startup Garage, BMW Group’s venture client unit, facilitated this collaboration and helped Monolith grow its business relations with the premium automotive manufacturer.
Using Monolith, BMW Group engineers built self-learning models using the wealth of their existing crash data and were able to accurately predict the force on the tibia for a range of different crash types without doing physical crashes. Moreover, the accuracy of the self-learning models will continue to improve as more data becomes available and the platform is further embedded into the engineering workflow. This game-changing approach now means engineers can optimise crash performance earlier in the design process and reduce dependence on time-intensive, costly testing whilst making historical data infinitely more valuable.
Dr Richard Ahlfeld, CEO and Founder, Monolith said: “When the intractable physics of a complex vehicle system means it can’t be truly solved via simulation, AI and self-learning models can fill the gap to instantly understand and predict vehicle performance. This offers engineers a tremendous new tool to do less testing and more learning from their data by reducing the number of required simulations and physical tests while critically making existing data more valuable. We are excited to see how BMW Group engineers are using pioneering technologies like Monolith to reduce the cost and time of product development as they develop the next generation of premium vehicles.”
The Monolith platform has been developed with a laser focus on user experience by automotive experts and data scientists to ensure seamless integration with existing engineering processes. As a result, as soon as the software is implemented, domain experts quickly begin gaining valuable insights and time back, as well as the chance to explore an even wider design space.
“What’s perhaps even more exciting than the promise of accelerating the vehicle development process is the opportunity for engineers to explore more design parameters and find new relationships between operating conditions without the need for data science support. Suddenly the combination of engineering expertise and machine learning becomes a competitive game-changer and gives our customers the means to create world-class products more efficiently.” Dr Ahlfeld continued.
The BMW Group is expanding its use of Monolith into more engineering functions across R&D that generate vast amounts of data from crash testing to aerodynamics, motorsports and advanced driver-assist systems (ADAS). Monolith is poised to swiftly scale, propelled by more globally renowned engineering teams adopting Monolith, an expert team, network of industry partners and an extensive portfolio of IP.