Digintel metasurface of extensive reprogrammable morphing

Programmable surface morphing is reshaping geometrical matter from static, prespecified forms into systems capable of adaptive, on-demand control, yet precise and extensive reprogrammable morphing of complex surfaces remains a central challenge through external stimulated material response. Here, we present a reprogrammable digital-intelligent (digintel) metasurface (DMS) built from digitally addressable bistable unit cells whose stable configuration switching generates local eigenstrain, as well as the digitally programmable surface morphing. By digitally encoding the stable state of unit cells, DMS exhibits extensive reprogrammable morphing with the complexity and type of surface morphology exponentially increasing with the number of unit cells. To precisely design the target surface, we further develop a machine learning-assisted inverse-design framework that maps the target surface to digital state codes, enabling deterministic reconstruction of complex surfaces. Experiments and finite element analyses validate the mechanical response of the unit cell and DMS, manifested as state-dependent stiffness, stability margins, load-bearing capacity, and morphing-enabled flow-field modulation across reconfigured geometries. This work establishes a generalizable platform for digintel engineering of reconfigurable functional surfaces, with potential impact on robotics, medical assistance, and aerospace morphing structures.