  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 a
series of manuscripts about 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) which 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 do 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
H1b suggesting that the conditions do significantly impact the relationship of
the covariates to one another and the structure of a covariate changes
significantly over time (t) given a set of initial conditions, respectively.
