IBM and Italian race car manufacturer Dallara Group have announced a collaboration to build AI models that speed up vehicle aerodynamic design. The project also looks at how quantum computing could play a role in simulation down the road.
IBM and Dallara to Advance AI and Quantum-Powered Design for High-Performance Vehicles
The work combines Dallara’s expertise in high-performance vehicle engineering with IBM’s leadership in AI for physics and quantum computing, to investigate how to ac… https://t.co/wGfM7FJqoS pic.twitter.com/KeCrwljbt0
— SolidLedger Studio (@YouSolidLedger) April 30, 2026
The partnership uses Dallara’s aerodynamic data, gathered over decades of real-world race car testing, to train an AI model. That data gives the model practical grounding from the start.
Early results are hard to ignore. A simulation that traditionally took several hours using computational fluid dynamics (CFD) methods was completed in about 10 seconds by the AI model. The accuracy was nearly identical to the standard approach.
International Business Machines Corporation, IBM
The initial testing focused on the rear diffuser geometry of a Le Mans Prototype 2-style race car. The AI evaluated hundreds of geometry configurations, identifying the same optimal design as CFD with similar error margins.
The practical upside is straightforward. Testing more design options early in development — before committing to expensive, full-scale simulations — could cut costs and shorten timelines.
Alessandro Curioni, IBM Fellow and VP of Algorithms and Applications at IBM Research, put it plainly: “Some of the hardest engineering challenges come down to accurately simulating the physical world.”
Dallara CEO Andrea Pontremoli framed the partnership in racing terms: “Racing has taught Dallara that there are two possible outcomes: you either win or are forced to learn.”
Beyond AI, the two companies are testing how quantum and hybrid quantum-classical approaches could slot into vehicle design workflows. It’s early days, but the goal is to tackle problems that today’s systems can’t handle efficiently.
The findings were published in a preprint study on arXiv on April 20, building on IBM’s Gauge-Invariant Spectral Transformers (GIST) model from a March 17 preprint. The companies also presented their work on April 26 at the International Conference on Learning Representations in Rio de Janeiro.
The companies plan to expand the AI models to cover additional scenarios, including different driving maneuvers and overtaking situations.
IBM stock fell 2.55% on Wednesday, closing at $227.10. The stock is currently trading near its 52-week low, down around 25% over the past six months.
That drop followed IBM’s most recent earnings report, where the company beat expectations on both earnings and revenue but held its guidance steady. The market didn’t love that, sending the stock down 9.25% on results day.
HSBC upgraded IBM to Hold from Reduce after the earnings dip, setting a price target of $231 and putting a $35 billion valuation on its quantum computing business. Stifel held its Buy rating with a $290 target, pointing to growth in IBM’s Red Hat and Data and AI segments.
Wall Street’s overall view sits at a Moderate Buy, based on 19 analyst ratings. The average price target is $298.44, which would represent a 31% gain from current levels.
IBM has also recently launched IBM Bob, an AI development platform for enterprise software teams, and expanded its partnership with MIT through the new MIT-IBM Computing Research Lab.
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