Abstract:

[Objective] In the process of solving ultra-high-speed collision problems, challenges such as significant material deformations and high computational costs are commonly encountered. In recent years, with the development of deep learning and the emergence of neural networks that incorporate physics knowledge, new ideas have been brought for the efficient solution of such problems. [Methods] This paper designs and implements an accurate simulation algorithm for ultra-high-speed collision problems, neural networks are used to learn the complex nonlinear mapping relationships between operators, thoroughly understanding the trends in changes among these operators. Based on this, physical prior knowledge is integrated, and the algorithm learns to predict and quantify the discrepancy between the predicted and real operators. A loss function is designed to achieve accurate simulation of each operator. [Results] Focusing on the Taylor bar collision case in ultra-high-speed collision problems, the error rate of operator fitting is less than 20%. [Limitations] There are many more cases in ultra-high-speed collision problems. By learning from a broader range of related cases, the applicability of the algorithm can be enhanced. [Conclusions] This paper designs a Taylor bar collision simulation algorithm based on deep learning neural networks combined with physical knowledge, and achieves good predictive performance.

Key words: deep learning, PINN, Taylor bar collision, operator, data-driven, scientific paradigm