Adaptive climate modeling with AI for smart selection of urban structure

Urban climate modeling faces a significant challenge: classical and hybrid AI-physics models lack contextual adaptability since they fail to alter their core physical representations to fit varied urban contexts. We introduce Adaptive Physics Selection (APS), a meta-modeling process that reconsiders AI as a system builder, allowing dynamic selection of ensembles of physics processes in alignment with real-time urban signatures. Synthetic archetype libraries and lightweight switch functions are utilized in APS to activate contextually dependent modules, particularly when dealing with uncertainty. Notably, APS represents an equity-oriented design process: it redistributes computational precision in highly vulnerable contexts, localises physics process prioritization with respect to spatial risks, and includes participatory governance. We argue that this shift from static simulation to adaptive physics choreography is essential in the creation of equitable and intelligent climate resilience in the Anthropocene.