pujachowdhury / physics-informed-machine-learning-example

A basic example of using physics informed machine learning for enhanced structural dynamics modeling

Physics-Informed-Machine-Learning-Example

A basic example of how physics informed machine learning can be used to enhance structural dynamics modeling.

Examples

License

This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa].

Sources

1. Barreau Matthieu, Physics-Informed Learning: Using Neural Networks to Solve Differential Equations, Online Lecture, Digital Futures: Research Hub for Digitalization, https://www.youtube.com/watch?v=R4ZvksarJ1Q&t=31s&ab_channel=DigitalFutures%3AResearchHubforDigitalization
2. Rudd, Keith. Solving partial differential equations using artificial neural networks. Diss. Duke University, 2013. https://www.proquest.com/docview/1477561981?pq-origsite=gscholar&fromopenview=true
3. Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational physics 378 (2019): 686-707. https://www.sciencedirect.com/science/article/pii/S0021999118307125
4. Chen, Qiuyi. "Physics Informed Learning for Dynamic Modeling of Beam Structures." (2020). https://repository.lib.ncsu.edu/bitstream/handle/1840.20/37410/etd.pdf?sequence=1