Software & Data Downloads — PhysicsInformedNeuralODE

Physics-Informed Neural ODE for modeling complex dynamical systems.

This repository includes source code for training and using the Physics-Informed Neural ODE (PINODE) Operator for modeling complex dynamics systems.

    •  Sholokhov, A., Liu, Y., Mansour, H., Nabi, S., "Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points", Nature Scientific Reports, DOI: 10.1038/​s41598-023-36799-6, Vol. 13, No. 1, pp. 10166, October 2023.
      BibTeX TR2023-136 PDF Software
      • @article{Sholokhov2023oct,
      • author = {Sholokhov, Aleksei and Liu, Yuying and Mansour, Hassan and Nabi, Saleh},
      • title = {{Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points}},
      • journal = {Nature Scientific Reports},
      • year = 2023,
      • volume = 13,
      • number = 1,
      • pages = 10166,
      • month = oct,
      • doi = {10.1038/s41598-023-36799-6},
      • url = {https://www.merl.com/publications/TR2023-136}
      • }

    Access software at https://github.com/merlresearch/PhysicsInformedNeuralODE.