这篇论文用PINN模拟钢-铝界面的弹性波传播，比传统模拟更快，还能预测新工况，挺实用的。
该研究提出了基于物理信息神经网络(PINN)的框架，用于模拟钢-铝双材料系统中的瞬态弹性波动传播，物理定律直接嵌入损失函数。使用ANSYS Workbench Explicit Dynamics进行高保真有限元仿真验证，并用作训练中的补充数据约束。PINN准确预测了波在界面上的透射和反射、轴向和径向位移历史、面平均响应及应力应变演化，与有限元结果高度一致。该网络还能预测未见时间点和修改材料参数后的波响应，无需额外仿真。网格敏感性研究证实了数值鲁棒性，其他材料组合验证了方法的通用性。
A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems
Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process. This study presents a PINN-based framework for modeling transient elastodynamic wave propagation in bimaterial systems governed by the axisymmetric equations of linear elasticity. A steel-aluminum specimen representative of a Split Hopkinson Pressure Bar configuration is considered, and the governing elastodynamic equations, together with the corresponding initial, boundary, and interface conditions, are incorporated directly into the network through a physics-informed loss function. High-fidelity finite-element simulations performed using ANSYS Workbench Explicit Dynamics are used for validation and as supplementary data constraints during training. The proposed framework accurately predicts wave transmission and reflection across the bimaterial interface and reproduces axial and radial displacement histories, face-averaged responses, and the dominant stress and strain evolution with close agreement to the finite-element solutions. The trained network further demonstrates the ability to predict wave responses at previously unseen time instants and for modified material properties without requiring additional finite-element simulations, providing a continuous surrogate model for elastodynamic analysis. Mesh-sensitivity studies confirm numerical robustness, while additional material combinations demonstrate the generality of the proposed methodology. The results show that integrating physics-informed neural networks with explicit finite-element analysis provides an accurate and computationally efficient framework for elastodynamic wave propagation in heterogeneous solids, offering an effective surrogate modeling approach for high-rate solid mechanics and impact engineering applications.