  Cold bent glass is a promising and cost-efficient method for realizing doubly
curved glass fa\c{c}ades. They are produced by attaching planar glass sheets to
curved frames and require keeping the occurring stress within safe limits.
However, it is very challenging to navigate the design space of cold bent glass
panels due to the fragility of the material, which impedes the form-finding for
practically feasible and aesthetically pleasing cold bent glass fa\c{c}ades. We
propose an interactive, data-driven approach for designing cold bent glass
fa\c{c}ades that can be seamlessly integrated into a typical architectural
design pipeline. Our method allows non-expert users to interactively edit a
parametric surface while providing real-time feedback on the deformed shape and
maximum stress of cold bent glass panels. Designs are automatically refined to
minimize several fairness criteria while maximal stresses are kept within glass
limits. We achieve interactive frame rates by using a differentiable Mixture
Density Network trained from more than a million simulations. Given a curved
boundary, our regression model is capable of handling multistable
configurations and accurately predicting the equilibrium shape of the panel and
its corresponding maximal stress. We show predictions are highly accurate and
validate our results with a physical realization of a cold bent glass surface.
