London-based AI software company Monolith uses an AI platform to substantially cut development and testing costs for new vehicles.

Monolith’s software uses self-learning models to instantly predict the results of complex vehicle dynamics systems, reducing the need for physical tests and simulations. The company claims this approach dramatically accelerates every stage of the automotive development process – from initial design through design iterations and validation to production that currently require repetitive, time-intensive and costly tests and simulations. Monolith states that with its platform, fewer physical prototypes and on-road testing are required, making product validation safer and more sustainable.

“Optimising a system, or finding a new solution based on a decade of historical data, is like instantly offering an engineer a decade of experience. That’s the power of AI – it supercharges an individual’s subject matter expertise by unlocking the expertise stored within a company’s data,” said Dr Joel Henry, Principal Engineer at Monolith.

So far, automotive companies have used a combination of life-like virtual simulations and physical testing during vehicle development. For each design iteration, a simulation solves the physics that underpins the system’s modelling – a notoriously difficult and computationally-intensive process. Virtual simulations help reduce the number of physical tests required, but the accuracy and fidelity of the results can be limited. Numerous physical tests are therefore still needed to calibrate and validate the virtual results, as well as to understand performance in operating conditions that cannot be simulated.

“Today, automotive companies are spending billions developing electrical architectures and software capabilities as they strive to win the race for electric, shared and autonomous mobility. This squeezes R&D budgets and product timelines in other areas, creating enormous pressure on the engineering teams working to develop higher quality vehicle hardware systems in less time and with fewer resources,” said Dr Richard Ahlfield, Monolith’s CEO and Founder.

Monolith spent six years developing its platform, which merges data from virtual and physical tests to train highly-accurate AI self-learning models. The models then predict the performance of systems by understanding their behaviour, instead of solving the complex physics of the system, or performing a physical test.

“Monolith was founded to empower engineers with AI to instantly solve even their most intractable physics problems. We know this resonates especially with automotive engineers who struggle to optimise hundreds of often conflicting criteria with hundreds of complex simulations. Requiring hours or days to solve, engineers have grown frustrated by the considerable amount of physical testing still required to make up for the limitations of the virtual tests. At the same time, the data that is created in the process represents an enormous opportunity when used with AI,” said Ahlfield.