Abstract: Rapid physical modeling of human soft tissue is crucial for surgical simulation. The integration of deep learning with finite element analysis addresses the inefficiencies of traditional modeling approaches. However, existing research primarily focuses on deformation modeling under external loading, neglecting the importance of other physical fields, such as stress and reaction forces, which play a critical role in guiding surgical training. To address this gap, we propose a novel neural network-based approach for rapid multi-physics modeling of soft tissues. This approach efficiently predicts physical fields by compactly encoding the nonlinear relationships between the loading conditions and mechanical responses of the soft tissue. To resolve the issue of non-convergence during network training caused by the scale differences between physical fields, we introduce the convolutional block attention module(CBAM)to dynamically balance the weights between different physical fields, thus overcoming the dominance of large-scale physical fields in model training. Extensive experiments show that the proposed method outperforms similar approaches in terms of both accuracy and efficiency in predicting multi-physics fields of soft tissue under external loads. Compared to traditional numerical methods, it achieves a several-thousand-fold improvement in efficiency with only about a 5% loss in accuracy, making it a promising tool for computer-aided medical technologies such as surgical simulation.

Key words: physical-based modeling, deep learning, surgical simulation, finite element model