Stochastic simulators are increasingly used to expand the frontier of scientific knowledge and inform decision-making across real-world contexts. Simulator calibration, a process by which internal model inputs are tuned to match some external criteria, usually in the form of observed data, is a key step in model design and validation. Epidemiological simulators present an especially compelling use case, as evidenced by the recent COVID-19 pandemic. Among several calibration paradigms, trajectory-oriented optimization is an emerging approach that does not require assumptions on the stochastic behavior of the simulator replicates and is particularly effective at identifying trajectories through the lens of errors between the simulator and observed data, especially when combined with Bayesian optimization. We present a tutorial on trajectory-oriented optimization with \texttt{adaptive\_ts}, an open-source Python package. We also provide a series of worked examples on an accompanying webpage.

翻译：随机模拟器正日益广泛地用于拓展科学知识前沿并为现实情境中的决策提供依据。模拟器校准（即调整内部模型输入以匹配某些外部标准，通常表现为观测数据）是模型设计与验证中的关键步骤。流行病模拟器提供了一个尤为引人注目的应用场景，这在近期的新冠疫情中得到了充分体现。在校准的多种范式之中，面向轨迹的优化作为一种新兴方法，无需对模拟器重复运行的随机行为做出假设，并且特别擅长通过模拟器与观测数据之间的误差视角来识别轨迹，尤其是在与贝叶斯优化结合时效果显著。本文提供了一份关于使用开源Python包`adaptive_ts`进行面向轨迹优化的教程，并在配套网页上提供了一系列实操示例。