Multiphysics-Guided Neural Networks Integrating Damage Mechanics and Thermodynamic Dissipation for PMMA Plate Impact Analysis Under Pressure Loading

The impact resistance of thin PMMA plates in deep-sea transparent structures is important for crashworthiness safety and is governed by high-strain-rate loading and hydrostatic pressure. Conventional empirical constitutive models exhibit limited adaptability to strain ratio effects and complex energy dissipation, leading to significant deviations in anti-impact assessment. This study proposes a constitutive-aware physics-informed neural network framework that integrates a data-driven constitutive neural network with embedded physical constraints. Such a proposed constitutive network replaces the theoretical model to capture rate-dependent stress response, while a physics-informed network enforces momentum conservation, energy balance, thermodynamic dissipation inequality, and pressure-induced stress limits. A dynamically weighted multimodal loss function is proposed and is used to ensure stable constraint enforcement during data training. Experimental validation based on a high-speed impact test confirms the constitutive and physics constraint network accuracy. Numerical results demonstrate that increasing hydrostatic pressure alters energy dissipation, increasing residual projectile velocities, and degrading impact resistance. The framework provides an efficient, physically consistent method for evaluating anti-impact performance of thin-walled PMMA structures in deep-sea engineering.