[Python] How to prevent overfitting in a Physics-Informed Neural Network (PINN) for a 1D...

I’m implementing a Physics-Informed Neural Network (PINN) in Python using TensorFlow to model a 1D underdamped harmonic oscillator. The network integrates the differential equation constraints directly into the loss function.

The issue I’m facing is that my model performs well on the training points but fails to generalize to unseen points in the domain. I’ve tried increasing the number of training points and adjusting the learning rate, but the overfitting persists.

The issue I’m facing is that my model performs well on the training points but fails to generalize to unseen points in the domain. I’ve tried increasing the number of training points and adjusting the learning rate, but the overfitting persists.

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