发表机构
University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对参数化一维浅水溃坝问题，对比了无需时间积分的PINN与TROM两种降阶模型，发现引入激波感知配点可提升PINN鲁棒性，为相关流体降阶模型应用提供了参考。
AI 中文摘要
我们开发了两种参数化数据驱动降阶模型：物理信息神经网络（PINN）与非侵入式张量降阶模型（TROM），并将两种方法应用于参数化一维浅水溃坝问题。两种降阶模型均无需时间积分，可学习从空间、时间及溃坝参数到物理状态的直接解映射。我们对样本外及外推参数值进行了详细对比，此外，证明引入激波感知配点对提升PINN模型鲁棒性至关重要。
英文摘要
We develop two parametric data-driven reduced models: a physics-informed neural network (PINN) and a non-intrusive tensorial reduced-order model (TROM), and apply both approaches to the parametrized one-dimensional shallow-water dam-break problem. Neither reduced model requires time integration: both learn a direct parameter-to-solution map from space, time, and dam-break parameters to the physical state, with the PINN providing predictions at arbitrary times and the TROM reconstructing solutions at the stored snapshot times. In addition, we demonstrate that it is essential to introduce shock-aware collocation to improve the robustness of the PINN model.
