A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems
arXiv:2607.06479v1 Announce Type: new 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 correspondin
The continuous advancements in AI and computational methods are enabling the application of neural networks to complex physics problems, improving simulation capabilities.
This development allows for more accurate and efficient modeling of material behavior under stress, critical for engineering design, material science, and potentially defence applications.
Traditional simulation methods for elastodynamic wave propagation may be augmented or even partially replaced by AI-driven approaches, offering faster and potentially more precise solutions.
- · Material scientists
- · Mechanical engineering sector
- · Defence industry
- · AI/ML researchers
- · Developers of legacy simulation software
- · Companies reliant on conventional finite element methods
Improved design and testing of complex materials and structures.
Reduced R&D cycles for products requiring advanced material performance.
Enhanced capabilities for predictive maintenance and failure analysis in critical infrastructure and equipment.
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Read at arXiv cs.AI