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Physics Informed Neural Networks for harmonic oscillator systems in underdamped, overdamped and critically damped case.

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Physics-Informed Neural Networks (PINNs)

In this notebook we investigate different PINN implementations for harmonic oscillators, covering all three different settings: (1) the underdamped case with δ < ω_0, (2) the critically damped case with δ = ω_0 and (3) the overdamped case with δ > ω_0.

Check out the notebook here!

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The following blog posts/articles were particularly useful:

  • Lagaris, I. E., et al., "Artificial neural networks for solving ordinary and partial differential equations", 1998.
  • Raissi, M., et al., "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations", 2019.
  • Moseley B., "So what is a Physics-Informed Neural Network?", 2021.
  • beltoforion.de, "Damped Harmonic Oscillator".

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