Multi-Layer Perceptron
Physics-Informed Neural Networks (PINNs)
DeepONet: Operator Learning with Neural Networks
Differentiable Simulation
Graph Neural Network
Lab Setup

Physics-Informed DeepONet Architecture

Key Modifications from Standard DeepONet

  1. Automatic Differentiation: Use PyTorch's autograd to compute derivatives
  2. Physics Loss: Incorporate PDE residual into training
  3. Boundary Enforcement: Soft constraints for boundary conditions
  4. Multi-objective Loss: Balance data fidelity and physics consistency
Back
 
Next
©  |   Cornell University    |   Center for Advanced Computing    |   Copyright Statement    |   Access Statement
CVW material development is supported by NSF OAC awards 1854828, 2321040, 2323116 (UT Austin) and 2005506 (Indiana University)