构建可捕捉激波的微喷管稀薄流快速代理模型
Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows
- 融合DeepONet架构学习参数依赖关系
- 引入激波对齐坐标系增强物理一致性
- 分阶段训练聚焦高梯度区域，提升精度
我们提出一个全面的、基于物理信息的深度学习框架，用于构建稀薄且含激波的微喷管流场的快速精确代理模型。该框架包含三个核心组件：用于捕捉参数依赖性的Fusion DeepONet算子学习架构；嵌入激波对齐坐标系的物理引导特征空间；以及强调高梯度区域的两阶段课程学习策略。为验证所提框架的通用性与归纳偏置能力，我们首先在具有对流陡化和类激波梯度的典型黏性Burgers方程上进行验证。
原文摘要 · Abstract (English)
We present a comprehensive, physics aware deep learning framework for constructing fast and accurate surrogate models of rarefied, shock containing micro nozzle flows. The framework integrates three key components, a Fusion DeepONet operator learning architecture for capturing parameter dependencies, a physics-guided feature space that embeds a shock-aligned coordinate system, and a two-phase curriculum strategy emphasizing high-gradient regions. To demonstrate the generality and inductive bias of the proposed framework, we first validate it on the canonical viscous Burgers equation, which exhibits advective steepening and shock like gradients.
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