PhysicsX emerges from stealth with $32M for AI to power engineering simulations

A lot of the buzz these days in artificial intelligence is around generative AI and how AI is being used to facilitate software and products for consumers. Now, an AI startup is called PhysicsXco-founded by two theoretical physicists – including a Formula One engineering superstar – emerged from hiding with a very specific focus on building and operating physical systems in the business world.

London-based PhysicsX has created an AI platform to create and run simulations for engineers working in project areas such as automotive, aerospace and materials science manufacturing – industries where there is often development bottlenecks due to how models are tested before production. It’s coming out of hiding now with $32 million in funding.

The round, a Series A, was led by General Catalyst. The rest of the round includes a very interesting mix of financial and strategic backers. It includes Standard Industries, NGP Energy, Radius Capital, and KKR co-founder and co-executive chairman, Henry Kravis. The funding will be used for business development, and to continue developing the company’s platform. This is PhysicsX’s first outside funding.

PhysicX tackles a very persistent but overlooked problem in the world of physical production.

In any physical system, in an experimental lab or a live industrial environment, every time a new idea is introduced – say, a theory about improving the operating efficiency of a piece of machinery, not to mention working on completely new products – engineers need to simulate how a new idea will work before committing to developing it, and further developing if how it works. Often, that simulation and testing work is carried out by scientists, engineers who may use some AI in the process but ultimately work the process manually.

“Something like air flow across an object might take you an hour or two, but if you want to simulate something more complex, it might take you a day or longer. So, there’s a computational cost and therefore also an hourly cost to it. And that limits the depth you can optimize,” said Robin Tuluie, who co-founded PhysicsX with Jacomo Corbo , in an interview.

The pair know the pain points very well first hand.

Tuluie now has two separate lives as a theoretical physicist. As an academic, he has worked with Nobel Prize winners studying astrophysics. He then moved into the world of racing, first at Renault and then at Mercedes, respectively as head of R&D and Chief Scientist, where he created designs that helped his teams win four Formula One world championships (earning some fame himself to process). He also spent years at Bentley and Volkswagen working on car design.

Corbo, who got his PhD from Harvard, also works in racing but recently he founded and heads QuantumBlack, McKinsey’s AI labs, working with many Formula One as well as other automotive and industrial companies. client on thorny product engineering problems. .

The pair brought together a group of no less than 50 scientists – other specialists in mechanical engineering, physicists and more – to build the PhysicsX platform, which faces the automotive but also a much wider applications, Corbo said.

“We are building an enterprise platform to support a fairly wide range of domain applications tied to construction and optimization problems, physics simulation bottlenecks,” he said. “What PhysicsX buys you is the ability to predict the physics (of a system) with very high accuracy and fidelity, doing it, anywhere from 10,000 to a million times faster. Now become much more sophisticated about, for example, mining, across a very high-dimensional space.”

The emergence of PhysicsX comes at the right time in the world of deep learning and AI, especially in how it is applied to the physical world.

Earlier this month DeepMind released new research on how it is applying the world’s most advanced machine learning to short- and long-term weather forecasting, and Corbo believes the physical turn will promote the next frontier of AI research and development.

“This is the first time that AI models, these deep learning models, these geometric deep learning models, have overtaken numerical simulation for the time being,” he pointed out. Corbo. “We’re starting to see that happen in physics more broadly. And, and that enables many different applications in the engineering space, so we’re building a platform to do that across sectors and across multiple problems in the domain.

Businesses, often, hit a lot of snags when it comes to digital transformation – tearing down existing infrastructure to use more modern IT and methods. Although you can classify what PhysicsX is doing as a kind of “digital innovation” too, the startup is able to avoid challenges, because the type of applications it faces, in engineering and R&D, unusual IT issues that require scaling across. organizations more widely.

All the same, it’s a new approach, and one that will disrupt how companies approach the development industry today. So General Catalyst is both betting on a very hot area – AI – but also breaking new ground by backing a startup that believes in how to develop the hot area.

“PhysicsX expands the boundaries of engineering in key sectors, led by a team highly skilled in simulation engineering and machine learning,” Larry Bohn, MD of General Catalyst, said in a statement. “With credibility, customer relationships, and technical expertise, we believe PhysicsX is poised to revolutionize engineering in complex industries. This aligns with our vision for industry transformation and positioning in PhysicsX with the opportunity to create a company that defines the category of advanced industries.

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