This paper introduces a novel approach to open-loop wave control in metasurface-programmable complex media by estimating the parameters of a compact physics-based forward model. The method significantly outperforms deep-learning-based digital twin benchmarks in terms of accuracy, compactness, and required calibration examples. Notably, the parameter estimation can be performed without phase information or measurements for all scattering coefficients, enabling non-coherent channel estimation and previously inaccessible wave control protocols. The approach is demonstrated in a metasurface-programmable chaotic cavity, achieving coherent wave control such as perfect absorption and phase-shift-keying backscatter communications with intensity-only measurements. The results highlight the potential of physics-based models in optimizing wave devices across various applications, including reconfigurable holographic antennas and signal processors.This paper introduces a novel approach to open-loop wave control in metasurface-programmable complex media by estimating the parameters of a compact physics-based forward model. The method significantly outperforms deep-learning-based digital twin benchmarks in terms of accuracy, compactness, and required calibration examples. Notably, the parameter estimation can be performed without phase information or measurements for all scattering coefficients, enabling non-coherent channel estimation and previously inaccessible wave control protocols. The approach is demonstrated in a metasurface-programmable chaotic cavity, achieving coherent wave control such as perfect absorption and phase-shift-keying backscatter communications with intensity-only measurements. The results highlight the potential of physics-based models in optimizing wave devices across various applications, including reconfigurable holographic antennas and signal processors.