PSA Groupe using Neural Concept to Optimize the Performance of Next Generation Vehicles
After evaluating the performance of Neural Network models on benchmark test cases (see Fig.1), PSA decided to push the collaboration further towards a real-time predictive model for external aerodynamics, applicable to production-level 3D simulations.
With this new step, PSA will aim at accelerating design cycles, the time between the ideation of a new design and the start of production. Another target is to optimize the performance of the next generations of vehicles, including larger autonomy and greater passenger comfort.
The benchmark study compared Geometric CNNs to Gaussian-Process based regression models, specifically tuned for production-level simulations.
It proved that, even though the Geometric CNN does not have access to the parametric description and is therefore much more broadly applicable than the Gaussian Process, it can also outperform the standard methods by a large margin.
Founded in 2019, Neural Concept provides the leading AI-first engineering platform for product development. By embedding AI natively into design and simulation workflows, Neural Concept empowers engineering teams to compress development cycles from months to days, improve product performance across efficiency, safety, and sustainability, and scale AI adoption without costly, years-long integration.
The company drives product development across major industries, including automotive, aerospace, energy, consumer electronics, semiconductors and defense, working with the world’s leading global OEMs and component suppliers. Neural Concept was spun out of the Swiss Federal Institute of Technology in Lausanne (EPFL) and is backed by global investors, including Forestay Capital and D. E. Shaw Ventures. Visit https://www.neuralconcept.com for more information.
Neural Concept is the link between designers and simulation experts in the company, reducing lengthy iterations between teams. This allows to dramatically accelerate R&D cycles, enhance product performances, and solve the most difficult engineering challenges.
