BMW Group engineers utilizing Monolith AI software program to foretell automobile efficiency earlier than design or testing begins


Monolith, an AI software program platform utilized by the world’s main automotive, aerospace and industrial engineering groups from Siemens to Honeywell, introduced that engineering groups on the BMW Group are utilizing its software program to speed up the event of their autos. By coaching Monolith self-learning fashions with the corporate’s engineering check knowledge, engineers can use AI to unravel extremely complicated physics challenges and immediately predict the efficiency of extremely complicated techniques akin to crash and aerodynamics exams.

The BMW Group crash check engineering workforce started working with Monolith in 2019 through the BMW Startup Storage to discover the potential of utilizing AI to foretell the power on a passenger’s tibia throughout a crash. Present crash growth makes use of hundreds of simulations in addition to bodily exams to seize efficiency. Even with refined modelling, owing to the complexity of the physics underpinning crash dynamics, outcomes require substantial engineering know-how to calibrate for actual world conduct.


BMW Monolith AI dashboard

Furthermore, bodily crash exams can solely be performed in later phases of growth when the design is mature sufficient to create bodily prototypes. Exploring a extra environment friendly resolution, the BMW Group collaborated with Monolith to see if AI may predict crash efficiency and importantly, considerably earlier within the automobile growth course of.

The BMW Startup Storage, BMW Group’s enterprise consumer unit, facilitated this collaboration and helped Monolith develop its enterprise relations with the premium automotive producer.

Utilizing Monolith, BMW Group engineers constructed self-learning fashions utilizing the wealth of their present crash knowledge and have been capable of predict precisely the power on the tibia for a variety of various crash varieties with out doing bodily crashes.

The accuracy of the self-learning fashions will proceed to enhance as extra knowledge turns into obtainable and the platform is additional embedded into the engineering workflow. Engineers can optimize crash efficiency earlier within the design course of and cut back dependence on time-intensive, pricey testing whereas making historic knowledge extra beneficial.

When the intractable physics of a fancy automobile system means it may possibly’t be really solved through simulation, AI and self-learning fashions can fill the hole to immediately perceive and predict automobile efficiency. This presents engineers an incredible new device to do much less testing and extra studying from their knowledge by lowering the variety of required simulations and bodily exams whereas critically making present knowledge extra beneficial.

We’re excited to see how BMW Group engineers are utilizing pioneering applied sciences like Monolith to scale back the associated fee and time of product growth as they develop the following technology of premium autos.

—Dr Richard Ahlfeld, CEO and Founder, Monolith

The Monolith platform has been developed with a concentrate on person expertise by automotive consultants and knowledge scientists to make sure seamless integration with present engineering processes. As quickly because the software program is applied, area consultants rapidly start gaining beneficial insights and time again, in addition to the prospect to discover a good wider design house.

What’s maybe much more thrilling than the promise of accelerating the automobile growth course of is the chance for engineers to discover extra design parameters and discover new relationships between working circumstances with out the necessity for knowledge science assist. Out of the blue the mix of engineering experience and machine studying turns into a aggressive game-changer and offers our clients the means to create world-class merchandise extra effectively.

—Dr Richard Ahlfeld

The BMW Group is increasing its use of Monolith into extra engineering features throughout R&D that generate huge quantities of information from crash testing to aerodynamics, motorsports and superior driver-assist techniques (ADAS).


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