An introduction to PINNs: Physics-Informed Neural Networks.
This repository is divided into three parts:
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Perceptron: It provides a basic introduction to Neural Networks (NNs). In particular, it develops an elementary model of a Perceptron variation, i.e., a single neuron.
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Backpropagation: It provides the Backpropagation method for a general Neural Network. In addition, it shows the maths behind this algorithm.
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PINNs: It introduces a class of NNs called Physics-Informed Neural Networks. In particular, it addresses some EDO examples using PINNs, which are implemented via the FLUX package.
This repository is based on different online courses, specific references can be found at the end of each notebook. Some of these courses are:
- Marquardt. (2021). Lectures Series of the course: Machine Learning for Physicists.(University of Erlangen-Nuremberg & Max Planck Institute for the Science of Light). https://machine-learning-for-physicists.org/
- Rackauckas, C. (2020). Introduction to Scientific Machine Learning through Physics-Informed Neural Networks. https://book.sciml.ai/course/
- Larrañaga, E. (2022). Notes of Computational Astrophysics course: Physics Informed Neural Networks (PINNs).