Table 2
Comparison of prior hybrid approaches and the present work.
| Dimension | Representative prior studies | This work |
|---|---|---|
| Physics Scope | Single-physics or weakly coupled models (thermal only; or thermal + cure without stress coupling). | Fully coupled thermos-chemo-mechanical formulation with porosity, resin flow, and stress evolution. |
| Multiscale Integration | Limited or absent microstructural coupling; macroscale only. | Explicit multiscale integration linking microstructural descriptors to macroscale PDE operators. |
| Machine-Learning Role | ML used as isolated surrogate or black-box correction without physical constraints. | ML embedded within a physics-informed operator framework with mathematically controlled blending. |
| Mathematical Guarantees | No proofs of existence, uniqueness, stability, or boundedness of hybrid models. | Theorem 1 establishes existence, uniqueness, and bounded model-data discrepancy. |
| Optimization Strategy | Parameter tuning via heuristic or gradient-free methods. | Adjoint-based optimization enabling efficient calibration of  |
| Uncertainty Quantification | Rarely includes global sensitivities or uncertainty-aware calibration. | Sobol-based sensitivity analysis combined Monte-Carlo propagation, and uncertainty-aware blending. |
| Real-Time Capability | Typically offline; no mechanism for adaptive correction. | Real-time compatible digital-twin updates with online ML adaptation. |
| Industrial Relevance | Validation often restricted to simple geometries or controlled labs. | Validation under representative processing conditions using high-fidelity thermo-chemo-mechanical datasets. |
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