This paper presents machine learning-based measurement models with state-augmenting contexts as a paradigm of dynamic data-driven application systems (DDDAS). In order to formulate well-posed statistical inference problems in realistic scenarios, one needs to identify and take into account all environmental factors and ambient conditions, called contexts, which affect sensor measurements. A kernel-based mixture modeling method carries out this task in an unsupervised manner, and results in a machine-defined context set and a probability distribution on it. The resulting measurement model is guaranteed to have contextual awareness, in the sense that the measurements are mutually independent conditioned on the system state and context. Numerical examples illustrate how contextual awareness improves inference performance in the setting of sequential target detection.