Machine learning and algorithmic approaches in ICF Capsule Design

In this talk we will discuss recent work in the UK on developing machine learning approaches to modelling and predicting the yield from NIF-like ICF implosions. We present several new ensembles of 10^3-10^4 simulations, showing that the uncertainty on predictions can be accurately decomposed into uncertainty from lack of data, and uncertainty on input parameters. We also show new approaches to finding novel classes of design with comparatively little human intervention. Finally we will briefly discuss how modern data science techniques are being used to support and maximise the utility of other types of HEDP experiments undertaken at the Central Laser Facility at the Rutherford-Appleton Laboratory.