This short communication documents the first attempt to apply the generalized finite difference method (GFDM) for inverse heat conduction analysis of functionally graded materials (FGMs). The fact that the GFDM is a meshless collocation method makes it particularly attractive in solving problems with complex geometries and high dimensions. By employing the Taylor series expansion and the moving least-squares technique, the method produces sparse and banded matrix which makes it possible to perform large-scale simulations. Three benchmark examples are provided to demonstrate the accuracy and adaptability of the GFDM approach in solving the inverse Cauchy problems. The convergence and stability of the method with respect to the amount of noise added into the input data are analyzed.

这段简短的交流记录了首次尝试将广义有限差分法（GFDM）用于功能梯度材料（FGM）的反向导热分析。GFDM是无网格搭配方法，这一事实使其在解决复杂几何形状和高尺寸问题方面特别具有吸引力。通过采用泰勒级数展开和移动最小二乘技术，该方法可以生成稀疏带状矩阵，从而可以进行大规模仿真。提供了三个基准示例，以证明GFDM方法解决柯西逆问题的准确性和适应性。分析了该方法相对于添加到输入数据中的噪声量的收敛性和稳定性。