用低成本模型+可解释修正，从稀疏数据重建高保真物理系统状态
Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Engines
- 融合快速低精度模型与学习到的可解释误差修正
- 从稀疏传感器数据成功重构旋转爆轰发动机高保真状态
- 适合需要实时监控与快速设计探索的多尺度复杂系统
在计算成本低的模型与复杂的物理系统之间弥合模拟到现实的差距，仍是机器学习在工程问题中的核心挑战，尤其在多尺度场景中，简化模型通常仅捕捉主导动力学。本文提出 Cheap2Rich，一种多尺度数据同化框架，通过结合快速低精度先验与学习到的可解释偏差修正，从稀疏传感器历史中重建高保真状态空间。我们在旋转爆轰发动机（RDEs）上验证了该方法，这类系统耦合爆轰波传播、喷注驱动非稳态、混合及刚性化学反应，跨越多个时间与空间尺度。所提方法成功从稀疏测量中重构出RDE的高保真状态，并分离出与喷注驱动相关的物理意义明确的偏差动态。结果表明，该框架具备通用性，适用于复杂多尺度系统的数据同化与系统辨识，支持快速设计探索、实时监测与控制，同时提供可解释的偏差动力学。代码已开源：github.com/kro0l1k/Cheap2Rich。
原文摘要 · Abstract (English)
Bridging the sim2real gap between computationally inexpensive models and complex physical systems remains a central challenge in machine learning applications to engineering problems, particularly in multi-scale settings where reduced-order models typically capture only dominant dynamics. In this work, we present Cheap2Rich, a multi-scale data assimilation framework that reconstructs high-fidelity state spaces from sparse sensor histories by combining a fast low-fidelity prior with learned, interpretable discrepancy corrections. We demonstrate the performance on rotating detonation engines (RDEs), a challenging class of systems that couple detonation-front propagation with injector-driven unsteadiness, mixing, and stiff chemistry across disparate scales. Our approach successfully reconstructs high-fidelity RDE states from sparse measurements while isolating physically meaningful discrepancy dynamics associated with injector-driven effects. The results highlight a general multi-fidelity framework for data assimilation and system identification in complex multi-scale systems, enabling rapid design exploration and real-time monitoring and control while providing interpretable discrepancy dynamics. Code for this project is is available at: github.com/kro0l1k/Cheap2Rich.
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