On-Demand Webinar

Winning Le Mans With Engineering Ingenuity & AI | Jota Sport


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Replacing 648 billion data points with AI

During a Le Mans race, the Jota engineering team produces 7500 data points per second…and as you probably know, the race lasts 24 hours. Attempting to analyse billions of data points in real-time to win the world’s most competitive car race is just the start of Jota’s challenges. Others include:

· Engineering a competitively aerodynamic car
· Optimising vehicle performance without sensor data
· Predicting tyre performance to optimise frequency and timing of pit stops

With only seconds separating those who take the podium from other competing teams, the winning edge comes down to engineering ingenuity – and visionary use of technology.

How did the unlikely team from Kent win Le Mans? Jota performance engineer Joao Ginete will go behind the scenes to demonstrate how their team replaced sensor data with self-learning models to measure and monitor tyre degradation and used AI and collective ingenuity to take the podium at the world’s most competitive motorsports race.


Learning objectives:


  1. Explain how to build self-learning models to reduce test scenarios
  2. Provide guidelines to engineers to identify good AI use cases
  3. Show how self-learning models can accelerate testing and increase ROI

Who should watch?

Engineers spending time doing repetitive, costly & time-intensive tests

Engineers working on cutting edge projects and products in engineering R&D

Engineers who want to test less, learn more, and explore their test data

Anyone interested in using self-learning models for complex systems

Meet our speakers


Joao Ginete

Joao has worked in Motorsports his entire career. After starting out as an aerodynamicist for two leading Formula One teams, he then moved to Race and Performance Engineering in various series like the European Le Mans Series, Japanese F3 and the World Endurance Championship. 




jousef webinar

Jousef Murad

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.




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