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Adaptive, predictive, uncertainty quantified models of dynamic building envelope technologies will be developed in Modelica and Python and connected through the FMI standard to link with other common practice software, specifically EnergyPlus (and possibly Grasshopper) for our studies.   The envelope model developed thus far is referred to as a dynamic network model with medium-fidelity. Network The model is constructed as a network  of volumes, individual modules, each module is a finite volumes with conservation laws to account for mass, momentum, and energy transfer within and between  connected through conservations laws. volumes. The behavior of installed systems with each module is derived from physical and data (experimental) analytics and applied to their effect on mass, momentum, and energy. Combining individual volumes, conservation laws, and system behavior, a dynamic network model is produced that bridges the gap between high-fidelity models, such as computation fluid dynamics, with low-fidelity models, such as building energy models, to create a parametric tool for design iteration and predictive modeling.  The capture and transformation of natural climatic energy flows at the building envelope is a vastly complex, interconnected physical problem of heat, mass, electromagnetic, and momentum transport.  
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Using advanced modeling methods of uncertainty quantification and pervasive adaptivity, critical knowledge gaps in the building sciences can be explored and leveraged to better improve our understanding, provide the best approximation, and project the impact of adaptive building envelopes.  Additionally, the improved results inform current and future energy modeling practices through co-simulation and the growing functional mock-up interface standard, with software like EnergyPlus and MatLab already functional.