###### Abstract  
Residual stress within a structural component can significantly affect the mechanical performance and stability of a structure. Therefore, it is crucial to find a way to determine the residual stress distribution to maintain the normal working of structures. Conventional methods for residual stress determination primarily include experimental testing, finite element simulations and inverse identification. However, these methods suffer from disadvantages of high testing costs, long calculation time and low inverse efficiency. To avoid these shortcomings, this study developed a high-performance method based on a deep learning technique. In this method, an artificial neural network was used to replace the finite element calculation in the finite element model updating (FEMU) technique and the residual stress distribution of structural components was inversely obtained based on the measured residual stresses of a finite number of measuring points. Compared with the conventional FEMU technique, the calculation efficiency of the proposed method was considerably improved. Furthermore, the accuracy and efficiency of the method were verified by simulated four-point bending experiments considering an elastic-plastic material. components. Thus, developing a method to obtain the complete residual stress field of the components is of great practical significance. The complete residual stress field of components can be obtained via interpolation [11], finite element simulations [12; 13], or inverse identification [14; 15]. The interpolation method is a mathematical calculation method, which does not consider the physical properties of materials, whereas the finite element simulation method has the disadvantage of long calculation time in predicting the residual stress under thermo-mechanical coupling conditions. A more accurate method to achieve the whole residual stress field is to determine the residual stress distribution which satisfies the elastic theory and is consistent with the residual stresses at the measuring points. Further, the inverse identification method combines an experimental test with the FEMU method [16; 17] and is expected to become an effective method for residual stress field prediction because its finite element calculation results in the residual stress distribution naturally satisfying the elastic theory. The inverse eigenstrain method is the most commonly used inverse identification method, using which the complete residual stress field can be reconstructed by updating the residual stress in the incompatible region [15]. However, if no prior knowledge of the region with residual stress distribution exists, the successful application of the inverse eigenstrain method is a challenge. In addition, most of the current inverse problems regarding residual stress use few parameters to describe the residual stress field, which may not be sufficient to accurately express its complex nature. Based on the FEMU method, this study attempts to develop a multi-parameter inverse method for residual stress identification that is suitable for a complex residual stress field without prior knowledge. However, for multi-parameter inverse identification problems, the inverse process often needs to call the finite element program several times iteratively and the resulting high time cost may make the inverse identification difficult to complete in a reasonable time. Therefore, developing a time-efficient simulation technology to replace the finite element calculation is crucial. The artificial neural network (ANN) is a possible alternative to the finite element program. By adjusting the weights of neurons, the ANN can automatically learn the complex mapping relationship between the input and output parameters. Further, it has an excellent nonlinear fitting ability and can realize almost real-time prediction. In fact, many data-driven research works have focused on using an ANN instead of a finite element analysis for real-time simulation [18; 19]. [PERSON] et al.