| Abstract: |
| The training process for the neural network problems may suffer from the super parameters and the non-convex formulation. Typical examples include the boundary condition imposition by penalty and the problem of saddle-point essence. This talk discusses how to impose the boundary condition for PINN and how to solve the saddle-point formulation physical model in friendly ways. The main proposal is to formulate the problems as convex energy-minimization problems to be friendly for optimizers. Though neural network problems are used for illustration, the methods essentially work for general meshless methods. |
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