Reproduction: Physics-Informed Neural Networks (Raissi et al., 2019)

[giovanna-britto]

Original article: Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019).
Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics.

PDF URL:
https://zenodo.org/records/18904388

Metadata URL:
https://github.com/giovanna-britto/Pinns-Schrodinger-Reproduction

Code URL:
https://github.com/giovanna-britto/Pinns-Schrodinger-Reproduction

Scientific domain:
Scientific Machine Learning / Computational Physics

Programming language:
Python (TensorFlow, PyTorch)

Suggested editor:

[rougier]

Thank you for you submission and totally sorry for the long delay, I did not see your submission. We'll assign an editor soon.

[rougier]

@gdetor Can you handle this submission?

[gdetor]

Hi @rougier sure I can handle this.

[rougier]

Thanks!

[gdetor]

Hi @mlosch would you be interested in reviewing this work?

[gdetor]

Hi @thmosqueiro would you be interested in reviewing this work?