  In railway transportation, the evaluation of track geometry is an
indispensable requirement to ensure the safety and comfort of railway vehicles.
A promising approach is to directly use vehicle dynamic responses to assess the
impact of track geometry defects. However, the computational cost of obtaining
the dynamic response of the vehicle body using dynamics simulation methods is
large. Thus, it is important to obtain the dynamic response of the
vehicle-track coupled system efficiently and accurately. In this work, a branch
Fourier neural operator (BFNO) model is proposed to obtain the dynamic response
of the vehicle-track coupled system. The model takes into account the nonlinear
relationship of the vehicle-track coupled system and realizes the fast and
accurate estimation of the system dynamic response. The relative loss (rLSE) of
BFNO model is 2.04%, which is reduced by 64%, compared with the traditional
neural network (CNN-GRU). In the frequency domain, BFNO model achieves the
effective estimation of the dynamic response of the system within the primary
frequency range. Compared with the existing methods, our proposed model can
make predictions at unseen time steps, enabling predictions from low to high
time resolutions. Meanwhile, our proposed model is superior to commercial
software in terms of efficiency. In the evaluation of track geometry, users can
use pre-trained BFNO to obtain the dynamic response with almost no
computational cost.
