Aerospace Engineering and Airspace Management
We help leading aerospace and airspace management organisations like NATS, Rolls-Royce, Airbus, and Certest take advantage of next-generation machine learning and data-centric engineering.
twinLab in Aerospace
Enabling AI-Adoption for Safety-Critical Systems
twinLab is a machine learning (ML) platform that makes it simple to build ML models specialised for advanced engineering applications. Powerful cloud-based functionality “under-the-hood” is seamlessly combined with accessible interfaces via Python, Excel, and API.
With twinLab, you can build high-fidelity digital twins and emulators of complex physical models and systems. Accelerate design cycles with active learning, increase understanding with inverse methods, and reduce risk with uncertainty quantification.
In Collaboration With:
Project Bluebird Case Study
Increasing the Efficiency of UK Air Traffic Control with AI
There has to be “a human in the loop” in safety-critical air traffic control systems. That is the Air Traffic Controller (ATCO). To properly understand where automation and AI can safely and reliably support them, NATS needed to know where AI performs well and where that performance wouldn’t negatively impact the ATCO’s work.
Our discovery process found that a digital twin for NATS would need to closely mirror the ATCO’s current environment, to mitigate risk and smooth the transition to AI-adoption. Using agile working practices, our software engineering team built a front-end, iterated on ATCO feedback, and hooked it up to the twinLab toolchain in order to model aeroplane behaviour using ML.
First of a kind solutions
digiLab is solving some of the world's biggest challenges in sustainability.
Whether it's supporting the design of the first fusion reactor, building the first electric aircrafts or reducing the chemical treatment of rivers by 50%, digiLab does not step back from the big, important challenges.