参数扫描的捷径
Shortcuts to Parameter Sweeps
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中文总结 AI 辅助
研究针对宽参数范围稳态敏感性评估效率问题，提出STPS控制策略，可通过单次受控扫描估计连续响应曲线，在单粒子和多体系统中效果良好，为随机模拟提供高效敏感性分析框架。
中文摘要 AI 辅助
在宽参数范围内高效评估稳态参数敏感性，对于识别有影响力的训练数据、拟合力场以及预测材料响应至关重要，但标准逐点方法需要反复弛豫和采样。本文引入参数扫描捷径（STPS），这是一种工程化控制策略，利用辅助控制在有限时间参数扫描过程中沿规定的瞬时稳态族传输概率密度。这使得可通过单次受控扫描，基于协方差响应关系估计整个参数区间上的连续响应曲线。STPS适用于平衡态和非平衡稳态系统，包括那些具有未知稳态分布的系统，且可在高维场景中直接利用稳态样本实现。对单粒子和相互作用多体系统的数值测试表明，STPS产生的响应曲线与参考结果高度吻合。这些发现确立了STPS作为随机模拟中用于连续敏感性分析的高效、基于样本的框架。
英文摘要
Efficient evaluation of stationary parametric sensitivities over broad parameter ranges is important for identifying influential training data, fitting force fields, and predicting material responses, but standard pointwise approaches require repeated relaxation and sampling. Here we introduce Shortcuts to Parameter Sweeps (STPS), an engineered control strategy that uses an auxiliary control to transport the probability density along a prescribed family of instantaneous stationary states during a finite-time parameter sweep. This enables the continuous response curve over the full parameter interval to be estimated from a single controlled sweep using covariance-based response relations. STPS applies to both equilibrium and nonequilibrium steady-state systems, including those with unknown stationary distributions, and can be implemented directly using stationary samples in high-dimensional settings. Numerical tests on single-particle and interacting many-body systems show that STPS yields response curves in close agreement with reference results. These findings establish STPS as an efficient, sample-based framework for continuous sensitivity analysis in stochastic simulations.
