Nuclear Fission

 

From accelerating decommissioning efforts and reducing closure costs, to augmenting the R&D capabilities of organisations developing the next-gen technologies, we’re evolving fission with a safety-critical lens.

No code required

twinLab in Fission

A Recommendation Engine for Sampling, Experiments, and Simulations

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.

Leverage twinLab’s active learning and Bayesian optimisation functionality to minimise the number of tests required for global sampling, reduce uncertainty across your systems, and reach the optimal viable solution, faster.

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In Collaboration With:

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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.

No code required

Nuclear Fission Case Study

Bayesian Approach to Nuclear Decommissioning

Read The Case Study

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