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Advances on Model Merging for Physics-Informed Neural Networks (PINNs)
Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE)Physics-Informed Neural Networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations by incorporating physical laws directly into neural network training. However, scaling PINNs to large domains and capturing high-frequency solution components remains challenging due to increased complexity and optimization ...
null Luiz Fernando Alves Macedo +2 more
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MFLP-PINN: A physics-informed neural network for multiaxial fatigue life prediction
European Journal of Mechanics, A/Solids, 2023Gaoyuan He
exaly
A transfer learning-physics informed neural network (TL-PINN) for vortex-induced vibration
Ocean Engineering, 2022Hesheng Tang, Yangyang Liao, Liyu Xie
exaly