Abstract
Polymer composites show complex, nonlinear mechanical behaviour. This makes their rational design hard. Running tests or high-fidelity simulations for every candidate design is slow and costly. Purely data-driven models are fast, but they can break basic mechanics outside the training range. Most physics-informed (PI) models, on the other hand, target a single constitutive law or failure mode, and their loss weights are set by hand. This study presents a DBN-GJO-PI-RNN surrogate that links deep feature learning, physics-constrained learning and failure screening in one workflow. A Deep Belief Network (DBN) learns compact latent features from the material and loading inputs. A PI Recurrent Neural Network (PI-RNN) then predicts the mechanical response under a composite loss with data, constitutive, equilibrium and boundary terms. Golden Jackal Optimization (GJO) tunes the network settings and the three loss weights together. Its fitness function rewards both accuracy and physical consistency. The predicted failure index is then checked against the onset criterion (FI ≥ 1). We tested the framework on a public dataset of 5,000 polymer-composite samples with six response targets. On the test set, it reached an MAE of 1.1132, an RMSE of 1.3962 and an R² of 0.9932. Its RMSE was 38.3% lower than that of the best purely data-driven baseline (TCN) and 50.6% lower than that of a standard RNN. The ablation study showed that each module reduced the error. The framework offers a fast, physics-aware surrogate for screening polymer-composite designs before costly tests or simulations.