Impacts of Multi-Dimensional Geometrical Uncertainties on Field Characteristics of Traveling-Wave Tube in Data-Driven Perspective

Recent prominent analyses on impacts of fabrication errors tend to start from Pierce parameters by assuming a normal distribution on them. We manage to analyze the complicated effects of multi-dimensional uncertainties directly from geometrical tolerances and present a general pattern for multi-dimensional geometrical uncertainty analysis on field characteristics. Aided by artificial neural networks (ANNs) which would get hold of the cost surface quickly and serve as rapid interfaces with a calculation speed several orders faster than full-wave code, Monte Caro analysis is accessible and effective to simulate the actual probabilistic distributions through a large-volume sampling on neural networks. Under such data-driven perspective which circumvents sophisticated theoretical analysis, the variations of geometrical parameters are analyzed as an entirety from the viewpoint of distribution. The dynamics of features of distributions of three field characteristics as well as geometrical parameters are therefore possible to be delved and analyzed. Such an analysis pattern is general since none of the steps presented cannot be readily transplanted to other topologies.

Automated summary

Recent analysis focuses on the impacts of fabrication errors by directly analyzing multi-dimensional uncertainties from geometrical tolerances, presenting a general pattern for multi-dimensional geometric uncertainty analysis in field characteristics. Using artificial neural networks to quickly grasp the cost surface and act as fast interfaces, Monte Carlo analysis is effective for simulating actual probabilistic distributions through large-volume sampling on neural networks. The data-driven perspective allows for analyzing variations of geometrical parameters as a whole from the perspective of distribution, thus enabling dynamic analysis of field characteristics and geometrical parameters.