Orateur
Description
Currently, dynamic aperture calculations of high-energy hadron colliders are
generated through computer simulation, which is both a resource-heavy and
time-costly process.
The aim of this research is to use a reservoir computing machine learning
model in order to achieve a faster extrapolation of dynamic aperture values. In
order to achieve these results, a recurrent echo-state network (ESN) architecture
is used as a basis for this work. Recurrent networks are better ?fitted to extrapo-
lation tasks while the reservoir echo-state structure is computationally e?ective.
Model training and validation is conducted on a set of "seeds" corresponding to
the simulation results of di?erent machine con?gurations. Adjustments in the
model architecture, manual metric and data selection, hyper-parameter tuning
(using a grid search method and manual tuning) and the introduction of new
parameters enabled the model to reliably achieve target performance on exam-
ining testing sets. Alternative readout layers in the model architecture are also
tried.
