Classical machine learning approaches are sensitive to non-stationarity. Transfer can address non-stationarity by sharing knowledge from one system another, however, in areas like prognostics and defense, data is fundamentally limited. Therefore, transfer algorithms have little, if any, examples which learn. Herein, we suggest that these constraints on algorithmic be addressed systems engineeri...