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Stiff-PDEs and physics-informed neural networks
In recent years, physics-informed neural networks (PINN) have been used to solve stiff-
PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D …
PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D …
Gnot: A general neural operator transformer for operator learning
Learning partial differential equations'(PDEs) solution operators is an essential problem in
machine learning. However, there are several challenges for learning operators in practical …
machine learning. However, there are several challenges for learning operators in practical …
Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads
A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …
Scalable transformer for pde surrogate modeling
Z Li, D Shu, A Barati Farimani - Advances in Neural …, 2023 - proceedings.neurips.cc
Transformer has shown state-of-the-art performance on various applications and has
recently emerged as a promising tool for surrogate modeling of partial differential equations …
recently emerged as a promising tool for surrogate modeling of partial differential equations …