用随机分层优化法高效校准等离子体表面反应模型，减少计算成本。
Stochastic hierarchical data-driven optimization: application to plasma-surface kinetics
- 基于简化海森矩阵识别关键参数子空间，减少模拟次数。
- 相比基线方法，样本效率显著提升，验证了有效性。
- 适合高复杂度反应系统建模，如等离子体与生化网络。
本文提出一种受松散模型理论启发的随机分层优化框架，用于高效校准物理模型。核心是采用简化海森矩阵近似，通过最少的模拟查询识别并聚焦于刚性参数子空间，从而高效导航高度各向异性的参数空间，避免全面采样的计算开销。为确保推断严格性，该方法结合概率框架，从观测数据直接导出原则性目标损失函数。通过等离子体-表面相互作用问题验证框架有效性：准确建模受限于表面反应活性参数的不确定性及动力学模拟的高计算成本。对比分析表明，该方法在样本效率上持续优于基线优化技术。本方法为复杂反应系统（涵盖等离子体化学到生化网络）的建模优化提供了通用且可扩展的工具。
原文摘要 · Abstract (English)
This work introduces a stochastic hierarchical optimization framework inspired by Sloppy Model theory for the efficient calibration of physical models. Central to this method is the use of a reduced Hessian approximation, which identifies and targets the stiff parameter subspace using minimal simulation queries. This strategy enables efficient navigation of highly anisotropic landscapes, avoiding the computational burden of exhaustive sampling. To ensure rigorous inference, we integrate this approach with a probabilistic formulation that derives a principled objective loss function directly from observed data. We validate the framework by applying it to the problem of plasma-surface interactions, where accurate modelling is strictly limited by uncertainties in surface reactivity parameters and the computational cost of kinetic simulations. Comparative analysis demonstrates that our method consistently outperforms baseline optimization techniques in sample efficiency. This approach offers a general and scalable tool for optimizing models of complex reaction systems, ranging from plasma chemistry to biochemical networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。