A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems
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, a
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- PossiblePossibly related (embedding) · 61%SciML/NeuralPDE.jl →
- LinkedLinked via arxiv author · 85%Sonal Ankush Chibire →
“A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems”
- LinkedLinked via arxiv author · 85%Jenn-Terng Gau →
“A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems”
- LinkedLinked via arxiv author · 85%Bo Zhang →
“A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems”
- PossiblePossibly related (embedding) · 49%NatLabRockies/phygnn →