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Despite their widespread use, they often fail to reproduce subtle appearance effects arising from complex light\u2013surface interactions, which have led to the emergence of specialized physics\u2010based models for specific optical phenomena (e.g., diffraction, iridescence, multilayers). Although more accurate, these models lose versatility and lack performance for real\u2010time rendering. Recently introduced, neural models have demonstrated their ability to approximate BRDF reference data coming from measurements, simulations, or even complex shading networks. However, most current neural models require relatively large networks, making them costly for real\u2010time rendering. In this paper, we introduce a hybrid model that combines a GGX\u2010type microfacet model and a neural model to leverage the best features of both representations. 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