IBM and Dallara to Advance AI and Quantum-Powered Design for High-Performance Vehicles

Rhea-AI Summary

IBM (NYSE: IBM) and Dallara announced a collaboration on physics-based AI foundation models and exploratory quantum integration to accelerate aerodynamic design for high-performance vehicles on April 30, 2026. Early models trained on Dallara's proprietary CFD data reduced some simulation runs from hours to about 10 seconds.

The teams plan wind‑tunnel and track validation next and published initial results in an arXiv preprint on April 20, 2026.

Positive

• AI surrogate reduced specific CFD evaluations from hours to about 10 seconds
• Early AI matched CFD optimal design with similar error margins
• Collaboration uses Dallara proprietary high‑fidelity aerodynamic simulation data
• Plans to integrate wind‑tunnel and track measurements for validation

Negative

• Wind‑tunnel and on‑track validation are future steps, not yet completed
• Results described as early/preliminary and limited to selected geometry tests
• No financial or commercial terms disclosed in the announcement

News Market Reaction – IBM

+1.71%

+1.71% Session close to close

In the Apr 30 session, IBM gained 1.71%, reflecting a mild positive market reaction.

Data tracked by StockTitan Argus on the day of publication.

Market Context

This announcement highlights IBM’s push into physics-based AI and quantum-assisted design, applying ...

Analysis

This announcement highlights IBM’s push into physics-based AI and quantum-assisted design, applying these capabilities to Dallara’s high-performance vehicle aerodynamics. It extends a series of recent AI collaborations in software, customer experience, and research. Investors may focus on how such projects translate into commercial offerings over time, how they complement IBM’s broader AI and quantum roadmap, and how quickly these techniques move from preprint results into real-world deployments.

Key Figures

IndyCar average speed: 230 mph IndyCar speed (metric): 370 km/h Racing heritage: 50+ years +5 more

8 metrics

IndyCar average speed 230 mph Typical track speeds in racing series IBM’s partner Dallara supports

IndyCar speed (metric) 370 km/h Metric equivalent of IndyCar average speeds cited for Dallara vehicles

Racing heritage 50+ years Dallara’s history designing and supplying high-performance vehicles

AI evaluation time 10 seconds Time for AI model to evaluate rear diffuser configurations vs CFD

Simulation reduction from many hours to few minutes Potential reduction in aerodynamic simulation time using AI models

Drag reduction impact 1–2% reduction Potential drag reduction across passenger vehicles mentioned as meaningful

arXiv preprint date April 20, 2026 Publication date of initial collaboration study on arXiv

GIST preprint date March 17 Preprint date for IBM’s Gauge-Invariant Spectral Transformers model

Previous AI Reports

5 past events · Latest: Apr 28 (Positive)

Same Type Pattern 5 events

Date	Event	Sentiment	24h Move	Catalyst
Apr 28	AI product launch	Positive	+2.2%	Introduced IBM Bob, an AI-first development partner for enterprise SDLC workflows.
Apr 21	AI partnership	Positive	+0.8%	Announced AI-powered experience orchestration solutions in collaboration with Adobe.
Apr 16	AI & quantum research	Positive	+2.5%	Expanded Discovery Accelerator Institute to advance AI and quantum computing research.
Mar 31	AI & quantum alliance	Positive	+2.2%	Launched a 10-year collaboration with ETH Zurich on AI and quantum algorithms.
Mar 25	AI voice integration	Positive	+0.3%	Integrated ElevenLabs speech capabilities into IBM watsonx Orchestrate for agentic AI.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Pattern Detected

Recent AI-tagged announcements have generally coincided with modest positive next-day moves, averaging about 1.6% across partnerships and research collaborations.

Recent Company History

Over the past month, IBM has issued several AI-focused announcements, including the launch of IBM Bob for software development, AI-powered experience orchestration with Adobe, and expanded AI and quantum research with the University of Illinois and ETH Zurich. These AI collaborations typically produced positive 24-hour price reactions between about 0.33% and 2.53%. The new Dallara partnership extends this pattern into high-performance vehicle aerodynamics and hybrid AI–quantum design.

Key Terms

physics-based ai foundation models, computational fluid dynamics (cfd), neural surrogate models, hybrid quantum-classical, +1 more

5 terms

physics-based ai foundation models technical

"IBM and Dallara are collaborating on the development of new physics-based AI foundation models."

Physics-based AI foundation models are large, general-purpose artificial intelligence systems that combine real-world physical laws (like motion, energy, or fluid flow) with data-driven learning so they can simulate and predict how complex physical systems behave. For investors, these models can cut development time, lower costs and risk, and unlock new products or efficiency gains in industries such as energy, manufacturing, robotics, and engineering—think of them as AI that learns from both examples and the rulebook of nature.

computational fluid dynamics (cfd) technical

"Engineers rely heavily on computational fluid dynamics (CFD), to predict aerodynamic forces..."

Computational fluid dynamics (CFD) is a computer-based technique that uses mathematical models to simulate how liquids and gases move and carry heat around objects, like a virtual wind tunnel or weather forecast for parts. For investors, CFD matters because it can cut physical testing, speed design cycles, reduce engineering surprises and compliance risks, and lower development costs—factors that influence product success, capital needs and time to revenue.

neural surrogate models technical

""High-performance vehicles are an ideal proving ground for neural surrogate models, but the potential impact goes well beyond the racetrack,""

Neural surrogate models are fast, trained computer programs that mimic the behavior of complex systems—like physical simulations, drug responses, or market dynamics—so users can get near-realistic results without running slow, expensive experiments. For investors they matter because these models can cut development time and cost, speed decision-making, and enable more testing of scenarios, potentially changing a company’s product timeline, risk profile, or competitive position.

hybrid quantum-classical technical

"starting to explore how quantum and hybrid quantum-classical approaches could further enhance race car design workflows."

A hybrid quantum-classical system pairs a quantum processor, which can perform certain types of complex calculations using quantum bits, with a conventional computer that runs ordinary software and coordinates the workflow. For investors it matters because this mixed approach is the most practical route to real-world gains from quantum computing: like using a specialist for the hardest plays while the rest of the team handles routine work, it can speed up specific tasks (optimization, simulation, cryptography) that may create new products, cost savings, or competitive risks.

gauge-invariant spectral transformers (gist) technical

"This work builds upon a new AI model developed by IBM, called Gauge-Invariant Spectral Transformers (GIST)..."

Gauge-invariant spectral transformers (GIST) are machine-learning architectures that mix frequency-based analysis with built-in symmetry protections so model outputs stay consistent when inputs undergo certain predictable transformations. They help algorithms focus on the true underlying patterns in complex, noisy data rather than incidental distortions. For investors, that can mean more stable forecasts and risk signals from time-series, network, or spatial data—like a camera filter that reveals a scene’s true layout regardless of viewing angle, improving automated decision quality.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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• IBM and Dallara are collaborating on the development of new physics-based AI foundation models.
• One early model was trained on Dallara's proprietary and validated aerodynamic data of a high-performance vehicle.
• Early results show the potential to reduce aerodynamics simulation time from many hours to few minutes and help engineers explore more design options earlier in vehicle development.
• The companies are starting to explore how to integrate quantum computing in the design workflow and further boost simulation fidelity for complex aerodynamic problems.

PARMA, Italy and NEW YORK, April 30, 2026 /PRNewswire/ -- IBM (NYSE: IBM) and the Dallara Group, a world-leading racing and high-performance vehicle manufacturer, today announced a collaboration to advance vehicle design and optimization using AI and explore the use of quantum computing. 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 accelerate aerodynamic design and open a path to even more advanced simulation workflows.

For more than 50 years, Dallara has designed and supplied high-performance vehicles for some of the world's top racing series, including IndyCar — where track speeds can average more than 230 mph (370 km/h) — as well as Formula 2, Formula 3, Super Formula, and Indy NXT, with additional work in top-tier series such as Formula E, WEC, and IMSA. This breadth of racing programs provides a unique ability to validate simulation results against real-world vehicle performance. Dallara also applies its engineering to high-performance road vehicles and aerospace. These and other distinctive, innovation-driven features of the company were key in IBM choosing to collaborate with Dallara.

As part of the project, IBM has been developing domain-specific foundation models in close coordination with Dallara. The models leverage not only Dallara's high-fidelity aerodynamic simulation data but also the company's deep technical expertise. In a future step, the teams aim to integrate validated measurements of real vehicles in wind tunnels and on the track, but the use of high-quality simulation data alone is already producing compelling early results.

Engineers rely heavily on computational fluid dynamics (CFD), to predict aerodynamic forces and optimize how vehicles perform across components such as body geometry, underfloor, wings, and wheels. These simulations are powerful but computationally expensive. Even relatively narrow analyses may take a couple of hours or more, while full race car development workflows may take weeks or months as engineers iterate through geometry changes, operating conditions, and performance tradeoffs.

IBM and Dallara are using AI to speed up those workflows without replacing the underlying physics. In one early example, which focused on the geometry of a conceptual Le Mans Prototype 2 (LMP2)-like race car, the two companies jointly compared CFD analyses of multiple configurations of the rear diffuser — a part located in the rear underfloor that helps generate efficient downforce and thus grip — with results from the new physics-based AI method.

The traditional approach took a few hours to calculate all the configurations. Meanwhile, the AI model completed the same evaluations in about 10 seconds, identifying the same optimal design with roughly the same error margins as CFD. Applied to a typical complete set of hundreds of geometry configurations, such a speedup could cut days of simulation time down to minutes.

These and other preliminary results suggest Dallara engineers can evaluate more vehicle configurations in a fraction of time to move faster in early design phases, helping focus their most expensive computational resources on deep-dive optimization of race car design and development.

In parallel, IBM and Dallara are starting to explore how quantum and hybrid quantum-classical approaches could further enhance race car design workflows. By combining Dallara's expertise in high-fidelity vehicle engineering and CFD-driven design with IBM's leadership in quantum computing and AI, the collaboration will evaluate where these methods can complement traditional simulation workflows in the near-term while identifying longer-term opportunities for practical use in automotive and motorsport design.

"Racing has taught Dallara that there are two possible outcomes: you either win or are forced to learn. IBM's close collaboration on this innovative project is a testament of Dallara's willingness to continuously push its boundaries and never stop learning," said Andrea Pontremoli, Dallara CEO.

"Some of the hardest engineering challenges come down to accurately simulating the physical world," said Alessandro Curioni, IBM Fellow and VP, Algorithms and Applications, IBM Research. "With Dallara, IBM is applying AI to speed up aerodynamic design today while advancing quantum computing in parallel to push simulation farther. Together, these technologies can help engineers move faster, explore more possibilities, and ultimately design better-performing vehicles."

Advancing aerodynamic design with AI

Designing a high-performance vehicle means balancing downforce, drag, stability, and responsiveness across conditions that can change from race to race. Because some parts are designed with exacting precision, even small design changes can lead to surprisingly large impacts on performance, and the best aerodynamic solution is not always obvious.

The AI models are being designed to help predict aerodynamic behaviors directly from geometry and related engineering inputs. As the collaboration progresses, IBM and Dallara plan to expand the AI models across a wider range of conditions, such as different maneuvers or overtaking scenarios, apply them to design new vehicles and develop tools that enable faster exploration of new aerodynamic configurations, before investing in intensive full-vehicle simulations.

"High-performance vehicles are an ideal proving ground for neural surrogate models, but the potential impact goes well beyond the racetrack," said Fabrizio Arbucci, Dallara CIO. "More efficient designs could benefit all transport categories, from passenger vehicles to aircraft, and even other industries at the mercy of aerodynamics. Even a one to two percent reduction in drag across passenger vehicles could add up to meaningful fuel-efficiency gains at scale."

Initial results of the collaboration are detailed in a preprint study published at arXiv on April 20, 2026. This work builds upon a new AI model developed by IBM, called Gauge-Invariant Spectral Transformers (GIST), which was described in a March 17th preprint study. IBM and Dallara presented these and other advances in applying AI to complex physical systems on April 26, 2026, at the International Conference on Learning Representations in Rio de Janeiro.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. IBM helps clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.

About The Dallara Group

Founded in 1972 by Giampaolo Dallara, Dallara is a world-leading manufacturer specializing in the design, engineering, and production of racing cars for top-tier motorsports. The firm has expanded globally from Italy's Motor Valley with a US Dallara Experience Hub in Speedway, Indiana. Dallara is the sole builder of racing cars for the IndyCar, Indy NXT, Formula 2, Formula 3 and Super Formula Championships, it also supplies Cadillac and BMW in both the WEC and IMSA championships. The expertise acquired in racing is regularly used both in the automotive world through consultancies and production services, with also Dallara branded products like the Dallara Stradale and Dallara^EXP and more recently in aerospace. Visit www.dallara.it for more information.

Media Contacts

IBM Research
Dave Mosher
IBM Research Communications
dave.mosher@ibm.com

Ashley Peterson
IBM Research Communications
ashley.peterson@ibm.com

Dallara
Andrea Vecchi
Dallara Marketing & Communications Director
a.vecchi@dallara.it

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SOURCE IBM

FAQ

What did IBM and Dallara announce on April 30, 2026 about vehicle design (IBM)?

They announced a collaboration to develop physics‑based AI models and explore quantum integration for aerodynamic design. According to IBM and Dallara, the work pairs Dallara's CFD data with IBM AI and quantum research to speed design and investigate higher‑fidelity simulation workflows.

How much faster was the AI method versus CFD in IBM and Dallara tests (IBM)?

One test showed the AI completed evaluations in about 10 seconds versus hours for CFD. According to IBM and Dallara, the AI identified the same optimal design with roughly similar error margins on the rear diffuser geometry tests.

Will IBM and Dallara use quantum computing in their vehicle design workflow (IBM)?

They are starting to explore quantum and hybrid quantum‑classical approaches to enhance workflows. According to IBM and Dallara, quantum methods are being evaluated for complementary use alongside traditional CFD and AI in near‑term and longer‑term studies.

Does the collaboration include validation with real vehicle measurements (IBM)?

Not yet; teams plan to integrate wind‑tunnel and on‑track measurements in a future step. According to IBM and Dallara, current early results use high‑quality simulation data and will expand validation with physical measurements later.

Where can investors read the technical results from the IBM and Dallara collaboration (IBM)?

Initial results are detailed in a preprint published on arXiv on April 20, 2026. According to IBM and Dallara, the preprint and related presentations given April 26, 2026, provide technical background on the AI models and early aerodynamic tests.