• DeepONet

  • Referenced in 50 articles [sw42093]
  • DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem ... practice, we propose deep operator networks (DeepONets) to learn operators accurately and efficiently from ... relatively small dataset. A DeepONet consists of two sub-networks, one for encoding the input ... partial differential equations, and demonstrate that DeepONet significantly reduces the generalization error compared...
  • ImprovedDeepONets

  • Referenced in 1 article [sw42601]
  • training dynamics of deep operator networks (DeepONets) through the lens of Neural Tangent Kernel ... provide new insights into the training of DeepONets and consistently improve their predictive accuracy...
  • PyTorch

  • Referenced in 440 articles [sw20939]
  • PyTorch python package: Tensors and Dynamic neural networks...
  • Adam

  • Referenced in 948 articles [sw22205]
  • Adam: A Method for Stochastic Optimization. We introduce...
  • DeepXDE

  • Referenced in 66 articles [sw32456]
  • DeepXDE: A deep learning library for solving differential...
  • PDE-Net

  • Referenced in 91 articles [sw36963]
  • PDE-Net: Learning PDEs from Data. In this...
  • DGM

  • Referenced in 185 articles [sw39282]
  • DGM: a deep learning algorithm for solving partial...
  • FPINNs

  • Referenced in 49 articles [sw40570]
  • fPINNs: Fractional Physics-Informed Neural Networks. Physics-informed...
  • NSFnets

  • Referenced in 26 articles [sw42059]
  • NSFnets (Navier-Stokes Flow nets): Physics-informed neural...