thermal-modeling

This paper proposes a domain-informed adaptive sampling framework for PINNs in metal additive manufacturing, using a conditional Flow Matching model to target high-residual collocation regions, achieving a 62.1% average reduction in relative L2 error over state-of-the-art baselines under an identical collocation budget.

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%。