More powerful AI tools will play a bigger part in the development of Formula 1’s aerodynamic rules as part of a new partnership between the FIA and Amazon Web Services.
AWS will be the FIA’s official artificial intelligence, cloud computing, machine learning and cloud infrastructure partner with the scope to shape future regulations with better aero simulations, assist with improvements to the FIA’s licensing systems globally, and be more integrated in how the FIA operates internally.
Running CFD testing with AWS is intended to unlock faster, higher-fidelity simulation on newer hardware, with AI shortcuts layered on top of that simulation and then AI checking of the rulebook itself.
Generative AI will not be used to prompt design ideas yet, though, as the priority is to use the tools to accelerate and support human work.
Using AWS at the core of the CFD work brings it into the FIA’s own virtual windtunnel and supports the creation of concept cars that can check things such as how much downforce is lost when following another car and whether the rules produce the racing the FIA wants.
The virtual process involved 3D digital drawings of car parts that are assembled into models, with the air around the car divided into millions of tiny cells so equations can be solved cell by cell, then post-processing turns raw numbers into pressure maps, flow visualisations and forces the FIA can analyse.
FIA deputy technical director for F1 Dominic Harlow explained: “AWS hardware builds that brief, so that’s a traditional CFD process run using OpenFOAM [an open-source software package] and various post-processing tools.
“Then alongside that, the enhancements to CFD data viewing and manipulation could come from AI.
“So we might look at using some surrogate approaches trained off our CFD data to shortcut the simulations, to get more productivity out of the whole process for less compute time, less engineering time, less energy consumption and less money.”
How it can change how cars are designed
Though the current ruleset is only in its first year, work is already under way for what could be a 2030 introduction for brand new cars and engines.
Harlow believes windtunnel, CFD and AI work will be cross-correlated to develop these rules, either to fill in the gaps in track data or simply to enhance the virtual development workload.
A full CFD run can take hours whereas a surrogate model as Harlow mentions would be an AI trained on hundreds of past CFD results, which learns the relationship between shape and outcome, then predicts the result for a new shape in seconds or minutes without solving the physics.
For the FIA that means testing many more variations of a draft rule – such as wing shapes and floor geometries – in the same time but the benefits extend to the non-aero side with the FIA’s performance department looking at electronics and engine regulations.
“So in performance, they’ve got tools like computer vision [image/video analysis], tools that enable us to dashboard a lot of metrics that we collect, some of the stuff for safety, some of the stuff for energy management, some of the stuff for legality checks and so on,” said Harlow.
“So a lot of the data that’s incoming to us, we can process more quickly using AI for trend monitoring, spotting patterns, basically, which is one of the things it’s good at.
“Similarly for the power unit, they use those kinds of tools in the PU [power unit] group for anomaly detection, so a neural net can represent any continuous function you like.
“If you give it enough neurons, you can plot the most complex squiggly line and then compare those squiggly lines with what’s coming off the track and you can see when somebody’s oil pressure’s dropped or whatever.”
A neural net is the basic AI structure, which would be big enough to model any pattern – and anomaly detection works by learning what normal traces look like then flagging anything that deviates from that.
This is more useful for informing FIA analysis as the AI tools are good at essentially predicting an outcome for a different event based on interrogating the data fed into it.
Building the model to learn as it goes will also turn the process from one-off rule-making to a continuous loop as the tools learn how any new regulations work in action and keep evolving.
This is useful for big rule changes when previous data does not accurately reflect the new cars although the caveat is that neural nets can be so-called black boxes, where the internal decision-making process is difficult or impossible for humans to trace, so the FIA would need to be able to understand why its tools have flagged something.
And with F1 cars being so complex, AI tools would be weak at making something entirely new for now, although the idea of ‘prompt engineering’ is something that could materialise further down the line.
“Already there’s work started on new aerodynamic concepts for future cars based on the learnings from these [2026] ones,” said Harlow. “Some different ideas that we could try and investigate.
“I suppose the generative part of AI is something we could imagine in the future, but at the moment it’s human-driven, engineer-driven.
“Prompt engineering your car, that’s obviously all about the training and the evolution of the models, and it takes a lot of computing and a lot of money to try and do that stuff for sure.
“I’m sure it’ll come.”
Broad benefits
One area the FIA should be able to gain in with its AWS partnership is accessing the latest GPU (graphics processing unit) hardware.
CFD traditionally ran on CPUs but the software is now being rewritten to run on GPUs, often several times faster and capable of handling huge numbers of simple calculations at once, which suits both AI and CFD. Better hardware means quicker, more accurate results.
Harlow gave the example of the porpoising effect that caught out several teams and the FIA at the start of the ground-effect era in 2022 as something that could potentially benefit from this.
“So instead of having to run just a round, steady-state CFD simulation, you can run a time-sensitive simulation in unsteady conditions,” said Harlow.
“And perhaps with a phenomenon like porpoising, get a bit more of an understanding before it’s a problem or before it’s seen in full size.”
Another area AI is likely to feed into the FIA’s processes is using large language models that are more fit-for-purpose to support team enquiries into legalities and stress-testing of regulations.
This means making a more private, secure version than ChatGPT or Claude-type AIs to ring-fence intellectual property, so confidential material stays inside the FIA but can be used to check such a vast, complicated rulebook and its interrelated clauses.
The breadth of uses also includes assisting dashboards managing safety, energy management and legality checks at the track, and supporting the enhanced remote operations centre in Geneva that supports race control remotely during events.
This may include assisting with track limits, as the current automated system can throw up hundreds of incidents that a human has to check whereas AI could provide a prioritised list or prompt a series of checks before a judgement is made so humans still decide, but do so faster and more consistently.
