  This paper presents a new approach to simulate forward and inverse problems
of moving loads using physics-informed machine learning (PIML).
Physics-informed neural networks (PINNs) utilize the underlying physics of
moving load problems and aim to predict the deflection of beams and the
magnitude of the loads. The mathematical representation of the moving load
considered in this work involves a Dirac delta function, to capture the effect
of the load moving across the structure. Approximating the Dirac delta function
with PINNs is challenging because of its instantaneous change of output at a
single point, causing difficulty in the convergence of the loss function. We
propose to approximate the Dirac delta function with a Gaussian function. The
incorporated Gaussian function physical equations are used in the
physics-informed neural architecture to simulate beam deflections and to
predict the magnitude of the load. Numerical results show that PIML is an
effective method for simulating the forward and inverse problems for the
considered model of a moving load.
