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.

该论文提出了一种新的物理信息机器学习(PIML)模拟移动载荷前向和反向问题的方法，通过利用物理信息神经网络(PINNs)来解决动载荷问题，并使用高斯函数替代Dirac delta函数，用于预测梁的弯曲和载荷量，仿真结果表明PIML是解决该问题的有效方法。

基于物理的机器学习用于运动载荷问题