Radiation transport and burn-up
Simulations exploring the degradation and evolution of advanced fuels, predicting not only the most likely outcome but the probability of all possible outcomes. Enabling safe optimisation of burn-up.
The Machine Learning Partner for Fission
From radiation transport and engineering design to material science and criticality analysis, our pioneering probabilistic methods enable your engineers and scientists to leverage expert opinion for safety-critical fission applications.
Organisations are transitioning towards capturing and utilising data to make decisions throughout the R&D cycle. digiLab have expertise in guiding organisations to do this effectively, as well as digital solutions for making informed and optimal decisions.
twinLab, our intuitive ML platform, helps engineers and scientists utilise ML alongside their current workflows. twinLab integrates with your scientific and engineering simulation tools and leverages Bayesian statistics to elicit expert knowledge.
Our Expertise in Fission
A lot of SMEs are specialising in AI and data science, but digiLab is the only one we know of built around state-of-the art methods of uncertainty quantification.
Explore our case studies and technical demos
Simulations exploring the degradation and evolution of advanced fuels, predicting not only the most likely outcome but the probability of all possible outcomes. Enabling safe optimisation of burn-up.
Leveraging transfer learning and physical constraints to optimise the knowledge gain of how materials will operate, while quantifying which physical properties are driving the uncertainty in operation.
Optimising the execution and simulation of large and expensive simulations to understand the possible outcomes.
UQ in systems level design enables an understanding of the risk posed by uncertainty in a system, and a way to direct resource to the significant source of that uncertainty.
How twinLab powers Fission?