Improve Race Car Performance Using Intelligent Algorithms | Jota Sport
Self-Learning Models for Complex Racing Systems
As an engineer, you want to use the best technology available to learn from all the data you collect, understand your physical relationships and ultimately increase your vehicle performance & reduce test times. Monolith provides a new AI solution enabling you to build self-learning, intelligent models to reduce the amount of testing to build & validate quality products faster.
This webinar presents how test engineers can use Monolith to quickly parameterize and model complex tests and reduce testing time by using the platform to optimise their test cycles, predict results and understand relationships and sensitivities between input parameters and their component/product performance, leveraging their past test experience and datasets.
Our guest speaker Joao Ginete, Jota Performance Engineer, shows how he and the test engineering team at Jota Sport use self-learning models to create continuous aero maps from data supplied by the manufacturer and how the same approach can be used to improve product life cycles and improve your test routine & predictions.
Monolith enables engineers all over the world to:
- Understand physically intractable problems
- Fully explore multiple virtual test scenarios
- Reduce costs and time investment throughout the whole R&D cycle
- Increase confidence in predictions & recommendations on which tests to run next