Parameter-tuning Networks: Experiments and Active Walk Model

TL;DR

This paper models the parameter-tuning process of complex experimental setups using networks and an active walk model, providing insights into human actions and optimization in experimental tuning.

Contribution

It introduces a novel active walk model that reproduces the network properties observed in real parameter-tuning processes, linking human tuning behavior to network dynamics.

Findings

Tuned ion source networks exhibit stretched exponential degree distributions.

The active walk model accurately reproduces experimental network properties.

Provides a new perspective on human-involved optimization processes.

Abstract

The tuning process of a large apparatus of many components could be represented and quantified by constructing parameter-tuning networks. The experimental tuning of the ion source of the neutral beam injector of HT-7 Tokamak is presented as an example. Stretched-exponential cumulative degree distributions are found in the parameter-tuning networks. An active walk model with eight walkers is constructed. Each active walker is a particle moving with friction in an energy landscape; the landscape is modified by the collective action of all the walkers. Numerical simulations show that the parameter-tuning networks generated by the model also give stretched exponential functions, in good agreement with experiments. Our methods provide a new way and a new insight to understand the action of humans in the parameter-tuning of experimental processes, is helpful for experimental research and other optimization problems.