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The computational inverse techniques can be used to convert the problem for internal characteristics, which are difficult to be determine, into external environmental parameters of complex structures. It provides an effective solution for many engineering problems. However, engineering problems are very complicated and the calculation scale is large, many inverse problems are ill-posed. The ill-conditioned system kernel matrix and the noise in the measurement response may severely affect the accuracy and stability of the inverse results. Since the artificial neural network does not rely on the selection of the initial value, it exhibits satisfactory ability of nonlinear mapping and global convergence. Based on the physical model and simulation model, Liu proposed a two-way trumpet neural network direct weight inversion theory. The analytic solution of the inverse problem was derived by an explicit formula for the first time. Theoretically, inverse problem can be directly solved by the weights and biases of the forward problem neural network and the explicit formula without training the inverse problem neural network. It greatly improves the computational inverse method efficiency. A special kind of Trumpetnet (Tubenet) successfully identified eight parameters in the mechanics of composites.

This paper systematically explores the two-way trumpetnet neural network inverse method, and attempts to overcome the ill-conditioned of the inverse system and functions. Specifically, the research contents of this article are as follows.

(1) A method of training an inverse problem neural network using a full trained forward problem neural network as a proxy model is proposed. The trained forward problem neural network is used as the forward problem solver to generate training data for the inverse problem neural network. More the samples required to the inverse problem neural network than the forward problem neural network is, the higher the computational efficiency of this method. This method improves the calculation efficiency while keeping up the completeness of forward problem output.

(2) Inverse calculation of composite material parameters is carried out based on the two-way trumpetnets neural network direct weight inverse method. This method is based on quasi-static numerical simulation experiments of composite laminates and structural response to perform inverse calculation of material parameters. The effects of activation function, training time and network structure on the accuracy of inverse were explored.

(3) Methods that can improve the sensitivity of parameters are explored by adjusting the layer angle of the composite material plate.