Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation.

中文翻译：

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以Apollo为例进行精确太空任务效用仿真的建模

提出了一种随机建模方法，可以实现快速而全面的太空任务效用仿真。该方法有助于同时进行贸易空间探索，风险评估和整体设计的多变量分析，同时探索，评估和发展经过统计验证的预期太空任务概念。通过统计机制，黑盒，贝叶斯，安萨兹和分析技术的协同集成来实现建模。通过蒙特卡洛模拟对阿波罗11–17号登月任务进行回测，从而验证了该方法能够准确描述人类太空飞行的能力，并验证了其执行任务效用分析的能力。