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The reconfigurability of radio environments with programmable metasurfaces isconsidered a key feature of next-generation wireless networks. Identifyingsuitable metasurface configurations for desired wireless functionalitiesrequires a precise setting-specific understanding of the intricate impact ofthe metasurface configuration on the wireless channels. Yet, to date, therelevant short and long-range correlations between the meta-atoms due toproximity and reverberation are largely ignored rather than precisely captured.Here, we experimentally demonstrate that a compact model derived from firstphysical principles can precisely predict how wireless channels in complexscattering environments depend on the programmable-metasurface configuration.The model is calibrated using a very small random subset of all possiblemetasurface configurations and without knowing the setup's geometry. Ourapproach achieves two orders of magnitude higher precision than a deeplearning-based digital-twin benchmark while involving hundred times fewerparameters. Strikingly, when only phaseless calibration data is available, ourmodel can nonetheless retrieve the precise phase relations of the scatteringmatrix as well as their dependencies on the metasurface configuration. Thereby,we achieve coherent wave control (focusing or enhancing absorption) andphase-shift-keying backscatter communications without ever having measuredphase information. Finally, our model is also capable of retrieving theessential properties of scattering coefficients for which no calibration datawas ever provided. These unique generalization capabilities of our pure-physicsmodel significantly alleviate the measurement complexity. Our approach is alsodirectly relevant to dynamic metasurface antennas, microwave-based signalprocessors as well as emerging in situ reconfigurable nanophotonic, optical androom-acoustical systems.

Experimentally realized physical-model-based wave control in metasurface-programmable complex media