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提出DAIST：一种可组合的AI加速迭代求解器，通过域分解将3D-IC全局热仿真拆分为块级子问题，用神经算子替代子求解器并经界面温度/热流通量迭代耦合，实现跨拓扑复用并在多芯片系统上相对FEM取得最高178倍加速，温度误差低至0.068%。
This paper presents a physics-informed neural network (PINN) framework for modeling transient elastodynamic wave propagation in bimaterial systems, using a steel-aluminum specimen from a Split Hopkinson Pressure Bar. The PINN accurately predicts wave transmission and reflection, validated against high-fidelity finite-element simulations, and serves as a continuous surrogate model for elastodynamic analysis.