UK startup PhysicsX, founded by former Formula 1 engineering whizz Robin “Dr. Rob” Tuluie, has unveiled an AI tool that could fast-track the time it takes to design a new aircraft from months to just a few days.
Dubbed LGM-Aero, the software creates new designs for aeroplanes. Using advanced algorithms trained on more than 25 million geometries, the model predicts lift, drag, stability, structural stress and other attributes for each shape. It then tailors the design according to what you want your plane to do.
PhysicsX said the AI is the first-ever Large Geometry Model (LGM) for aerospace engineering. A barebones version of the model, Ai.rplane, is also accessible free of charge.
“This is a first step in transforming the way engineering is practised in advanced industries [like automotive, aerospace, and manufacturing],” said Tuluie, founder and chairman of PhysicsX.
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“Over time, we will bring new capabilities to LGM-Aero and Ai.rplane, allowing users to select powertrains, add controls and further content to reach mature designs in days rather than months or years,” he said.
Tuluie wasn’t always an entrepreneur. For the first half of his life, he worked alongside Nobel Prize winners as an astrophysicist. Then, at 41, he entered the F1 scene where he devised designs that helped Renault, and later Mercedes, win four Formula One world championships between them.
In 2019, Tuluie founded PhysicsX alongside Jacomo Corbo, a Harvard-educated engineer who ran McKinsey’s AI lab. Together, the duo have assembled a 50-strong team of some of the world’s top minds in data science, AI, and machine learning.
PhysicsX, based in London, emerged from stealth in November 2023 with €30mn in funding. The company is on a mission to reimagine simulation for science and engineering using AI in sectors such as automotive, aerospace, and manufacturing.
PhysicsX says it is looking to help engineers better anticipate design bottlenecks, such as the drag of a new aeroplane or car design before they set out on building a physical prototype — saving them time and money. Its software acts like a supercharged wind tunnel for ideas.
“In the same way that large language models understand text, Ai.rplane has a vast knowledge of the shapes and structures that are important to aerospace engineering,” explained Corbo.
“The technology can optimise across multiple types of physics in seconds, many orders of magnitude faster than numerical simulation, and at the same level of accuracy.”
Corbo called LGM-Aero “an important stepping stone” towards developing physics foundation models. These are AI systems designed to simulate and solve complex physical problems by learning patterns from data and physical laws.
Applying AI to complex scientific problems is gaining traction. In 2020, Google Deepmind’s Alphafold model famously cracked a puzzle in protein biology that had confounded scientists for centuries. The discovery has accelerated research in drug discovery, molecular biology, and bioengineering.
Other companies, like Dutch scaleup VSParticle, are using algorithms to fastrack the discovery and synthesis of potentially game-changing materials.
While the applications of AI in science may differ from discipline to discipline, the benefits are shared: artificial intelligence can supercharge scientific discovery by analysing data, simulating complex systems, and uncovering insights faster than humans ever could.
So AI isn’t all about asking ChatGPT what to eat for dinner? No, dear reader, it’s a actually pretty big deal.
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