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

Physics-Informed Training

Multi-Component Loss Function

The physics-informed loss combines three components:

  1. Data Loss: Ensures predictions match true solutions
  2. Physics Loss: Enforces PDE: \(\frac{d^2u}{dx^2} + f(x) = 0\)
  3. Boundary Loss: Enforces boundary conditions: \(u(0) = 0\), \(u(1) = 0\)
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CVW material development is supported by NSF OAC awards 1854828, 2321040, 2323116 (UT Austin) and 2005506 (Indiana University)