Hi, I’m Juan Diego Toscano. Thanks for stopping by.

This repository will help you get involved in the physics-informed machine learning world. In particular, it is a step-by-step guide that covers some basic concepts needed to run a PINN in Pytorch (from approximating functions to solving PDEs).

Right now, I am a bit busy. However, in the future, I will upload more examples on inverse problems, DeepOnets, and other types of PINNs.

If you have any questions or if I can help you in some way, please feel free to reach me at: [email protected].

Note: Most of the examples in this repository were taken from the DeepXDE library (https://deepxde.readthedocs.io/en/latest/).

[1] Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2017). Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561. http://arxiv.org/pdf/1711.10561v1

[2] Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (1907). DeepXDE: A deep learning library for solving differential equations,(2019). URL http://arxiv. org/abs/1907.04502. https://arxiv.org/abs/1907.04502

[3] Rackauckas Chris, Introduction to Scientific Machine Learning through Physics-Informed Neural Networks. https://book.sciml.ai/notes/03/

[4] Repository: Physics-Informed-Neural-Networks (PINNs).https://github.com/omniscientoctopus/Physics-Informed-Neural-Networks/tree/main/PyTorch/Burgers'%20Equation