Driving efficiency in engine calibration with machine learning

Machine learning meets set-based design: A practical approach to overcoming complexity in vehicle design and simulation

On our minds: Women in engineering

Enhancing set-based design with advanced machine learning

Unlocking the potential of set-based design with machine learning

Tackling e-powertrain calibration complexity with advanced machine learning

Unlocking more efficient design with Secondmind open source tools

Applying generative AI to solve complex automotive engineering problems

Introducing compiler optimizations for memory-safe & democratized modelling

Mixture-model likelihoods for outlier detection

Achieving sustainability through machine learning

GPflux: A Library for Deep Gaussian Processes

Trieste: our first open-source toolbox

What is Stochastic Network Control?

What is a Gaussian process?

Sparse GPs: approximate the posterior, not the model

A guide to bugs: where to find them and what to do with them

Cloud-based ML: the catalyst transforming automotive product development