This work presents a computational framework for parametric multiscale surrogate modeling of polycrystalline materials. The framework integrates a physics-based Deep Material Network (DMN), specifically an Orientation-aware interaction-based Deep Material Network (ODMN), with two data-driven components: a Texture-Adaptive Clustering and Sampling (TACS) scheme that provides a reduced yet statistically consistent representation of crystallographic texture, and a Graph Neural Network (GNN) that infers the micromechanical equilibrium parameters of the ODMN from grain-level interaction graphs. Referred to as the TACS-GNN-ODMN framework, this combination introduces a microstructure-to-parameter mapping that constructs fully parameterized surrogate models for previously unseen microstructures without retraining. The resulting surrogate model preserves the micromechanical structure of the underlying formulation while substantially reducing computational cost. Numerical results show that the framework accurately predicts nonlinear mechanical responses and crystallographic texture evolution under several loading conditions, in close agreement with full-field direct numerical simulations (DNS). The method further achieves more than two orders of magnitude speed-up over fast Fourier transform (FFT)-based simulations. The framework thus provides an efficient and physically consistent strategy for multiscale modeling of polycrystalline materials and is well suited to large-scale simulations and real-time applications.

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