🤖 AI Summary
This work investigates the reflection and transmission of transient elastic waves at the interface of a bimaterial system. A physics-informed neural network (PINN)-based surrogate model is proposed, which embeds the axisymmetric linear elastodynamic equations, initial and boundary conditions, and interfacial constraints directly into the loss function, and is trained using data from ANSYS explicit dynamics finite element simulations. To the best of our knowledge, this is the first application of PINNs to modeling elastic wave propagation in bimaterial systems under high strain rates, enabling generalization across time instances and material parameters without additional simulations. The model accurately reproduces displacement time histories, surface-averaged responses, and stress–strain evolution, showing excellent agreement with finite element results, and its robustness is confirmed through mesh sensitivity analysis.
📝 Abstract
Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process. This study presents a PINN-based framework for modeling transient elastodynamic wave propagation in bimaterial systems governed by the axisymmetric equations of linear elasticity. A steel-aluminum specimen representative of a Split Hopkinson Pressure Bar configuration is considered, and the governing elastodynamic equations, together with the corresponding initial, boundary, and interface conditions, are incorporated directly into the network through a physics-informed loss function. High-fidelity finite-element simulations performed using ANSYS Workbench Explicit Dynamics are used for validation and as supplementary data constraints during training. The proposed framework accurately predicts wave transmission and reflection across the bimaterial interface and reproduces axial and radial displacement histories, face-averaged responses, and the dominant stress and strain evolution with close agreement to the finite-element solutions. The trained network further demonstrates the ability to predict wave responses at previously unseen time instants and for modified material properties without requiring additional finite-element simulations, providing a continuous surrogate model for elastodynamic analysis. Mesh-sensitivity studies confirm numerical robustness, while additional material combinations demonstrate the generality of the proposed methodology. The results show that integrating physics-informed neural networks with explicit finite-element analysis provides an accurate and computationally efficient framework for elastodynamic wave propagation in heterogeneous solids, offering an effective surrogate modeling approach for high-rate solid mechanics and impact engineering applications.