This study explores the use of Machine Learning (ML) in the field of Human Resources Management (HRM) alternatively, Human Capital Management (HCM), through a unique approach of employing partial differential equations (PDEs) to address the complexity of anthropomorphic systems. The mathematical representation offers a robust evaluation of human activities and demonstrates the potential of Bayesian-based machine learning techniques for visual representation in predictive analytics applications. This study is a part of Scientific Machine Learning (SciML), a method that uses partial differential equations to represent physical systems and domain-specific data. In this text, the data are from non-stationary environments with polymorphic uncertainty. The hypotheses tested in this study are: H1a (null hypothesis) states that the structure of a covariate does not change significantly over time (t) given a set of initial conditions, while H1b (alternative hypothesis) states that the structure of a covariate changes significantly over time (t) given a set of initial conditions. H2a (null hypothesis) states that the conditions do not significantly impact the relationship of the covariates to one another, and H2b (alternative hypothesis) states that the conditions significantly impact the relationship of the covariates to one another. The models use linear regression analysis with targeted productivity as the dependent variable and date as the independent variable. The results show that the relationship between targeted productivity and date is statistically significant, providing evidence to support H2b and suggesting that the conditions do significantly impact the relationship of the covariates to one another. This study highlights the importance of considering the impact of conditions on the relationship between covariates when analyzing data that changes over time.

翻译：本研究探索了一种独特的机器学习方法在人力资源管理（HRM）或人力资本管理（HCM）领域的应用，通过采用偏微分方程（PDEs）来解决拟人系统的复杂性。该数学表征提供了对人类活动的稳健评估，并展示了基于贝叶斯的机器学习技术在预测分析应用中用于视觉表征的潜力。本研究属于科学机器学习（SciML）范畴，该方法利用偏微分方程来表征物理系统和特定领域数据。本文中的数据来自具有多态不确定性的非平稳环境。本研究检验的假设为：H1a（零假设）指出，在给定初始条件集合的情况下，协变量的结构不会随时间（t）发生显著变化；而H1b（备择假设）则指出，在给定初始条件集合的情况下，协变量的结构会随时间（t）发生显著变化。H2a（零假设）指出，条件不会显著影响协变量之间的关系；而H2b（备择假设）则指出，条件会显著影响协变量之间的关系。模型采用线性回归分析，以目标生产率为因变量，日期为自变量。结果表明，目标生产率与日期之间的关系具有统计显著性，这为支持H2b提供了证据，并表明条件确实显著影响了协变量之间的关系。本研究强调了在分析随时间变化的数据时，考虑条件对协变量关系影响的重要性。