  Recently, it has been widely accepted by the research community that
interactions between humans and cyber-physical infrastructures have played a
significant role in determining the performance of the latter. The existing
paradigm for designing cyber-physical systems for optimal performance focuses
on developing models based on historical data. The impacts of context factors
driving human system interaction are challenging and are difficult to capture
and replicate in existing design models. As a result, many existing models do
not or only partially address those context factors of a new design owing to
the lack of capabilities to capture the context factors. This limitation in
many existing models often causes performance gaps between predicted and
measured results. We envision a new design environment, a cyber-physical human
system (CPHS) where decision-making processes for physical infrastructures
under design are intelligently connected to distributed resources over
cyberinfrastructure such as experiments on design features and empirical
evidence from operations of existing instances. The framework combines existing
design models with context-aware design-specific data involving
human-infrastructure interactions in new designs, using a machine learning
approach to create augmented design models with improved predictive powers.
