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MN-PINN: Physics-informed neural network for dynamic simulation of vehicle-bridge systems
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DOI：10.1016/j.advengsoft.2026.104195.png)
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Abstract
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• A novel PINN (MN-PINN) for dynamic simulation of vehicle-bridge systems is proposed. • Encoding dynamics of complex structural systems into neural networks is realized. • The proposed MN-PINN is validated against numerical results from 2D and 3D vehicle-bridge systems. • The robustness and generalization capability of MN-PINN are evaluated.
Keywords:
MN-PINN
vehicle-bridge systems
physics-informed neural networks
dynamic simulation
structural dynamics
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