JAREX：一种用于多目标算法过程表征的采集函数
JAREX: An Acquisition Function for Multi-Objective Algorithmic Process Characterization
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中文总结 AI 辅助
JAREX提出一种贝叶斯主动学习采集函数，通过联合边界学习与乐观可行性掩码，高效恢复多目标过程通过区域，相比传统DOE显著提升样本效率并减少实验次数。
中文摘要 AI 辅助
药物过程表征是质量源于设计的核心，因为它定义了过程参数的变化如何影响满足产品质量标准的能力，从而支持公认的可接受范围和稳健的制造。然而，在实践中，表征仍然主要依赖于因子设计的实验设计（DOE）方法，这些方法在解决高维空间中的多变量通过/失败边界方面效率低下。虽然贝叶斯优化已经改变了过程优化，但用于多目标过程表征的自适应方法仍然缺乏。在此，我们介绍了JAREX（联合可接受区域探索），一种用于多目标过程表征的贝叶斯主动学习采集函数。JAREX将表征表述为一个联合边界学习问题，并自适应地选择实验来恢复由多个目标上阈值标准的同时满足所定义的联合通过区域。JAREX将乐观的联合可行性掩码与随机跨越的多目标扩展相结合，将采样集中在失败的联合边缘。我们的基准研究表明，在整个实验预算范围内，JAREX比因子DOE、空间填充设计和贪婪的逐目标策略提供了更准确和样本高效的联合通过区域恢复。对于批量实验，它将迭代过程表征实验的数量减少了一半以上，同时保持了边界识别任务的高精度。JAREX在开源obsidian包中实现，为自适应、数据高效的多目标算法过程表征提供了一个模块化框架，支持高维空间中的样本高效范围查找。
英文摘要
Pharmaceutical process characterization is central to Quality by Design because it defines how variations in process parameters affect the ability to meet product quality specifications, thereby supporting proven acceptable ranges and robust manufacturing. In practice, however, characterization still relies largely on factorial design of experiments (DOE) approaches, which are inefficient for resolving multivariate pass/fail boundaries in higher-dimensional spaces. While Bayesian optimization has transformed process optimization, adaptive methods for multi-objective process characterization remain lacking. Here, we introduce JAREX (Joint Acceptable Region EXploration), a Bayesian active-learning acquisition function for multi-objective process characterization. JAREX formulates characterization as a joint boundary-learning problem and adaptively selects experiments to recover the joint pass region defined by simultaneous satisfaction of threshold criteria across multiple objectives. JAREX combines an optimistic joint-feasibility mask with a multi-objective extension of randomized straddle, focusing sampling on the joint edge of failure. Our benchmark study suggests that JAREX provides more accurate and sample-efficient recovery of the joint pass region than factorial DOE, space-filling designs, and greedy objective-wise strategies over the full experimental budget range. For batched experimentation, it reduces the number of iterative process characterization experiments by more than half while preserving high accuracy for the boundary-identification task. Implemented in the open-source obsidian package, JAREX provides a modular framework for adaptive, data-efficient multi-objective algorithmic process characterization, supporting sample-efficient range finding in high-dimensional spaces.
发表机构
- Merck & Co., Inc.（默沙东公司）
机构由 AI 辅助整理，请以论文原文为准。
