Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

McMahon Lab

Last update: Jan 5, 2023

Related tags

Deep Learning Physics-Aware-Training

Overview

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)¹ to train Physical Neural Networks (PNNs) - neural networks whose building blocks are physical systems.

This repository is a PyTorch-based implementation of Physics-Aware Training. It lets users build Physical Neural Networks and automates many of the necessary steps to train them with Physics-Aware Training. To use an existing physical system as a building block in a neural network, users have to supply a class that receives batches of input data and processes them in the physical system. After specifying the trainable parameters, the system can be trained with this code. The methodology is demonstrated on an illustrative example of simulated, nonlinear coupled pendula. In our paper, we demonstrated the method on real experiments.

This repository also gives users access to documented reference code to implement or modify PAT.

Getting started

To learn about Physical Neural Networks, Physics-Aware Training, and the scope of this repository, have a look at the Introduction.
To see pedagogical examples with simulations of real experiments, head over to the Demonstrations.
To apply PAT to new experiments, see How to make it work with your experiments.
To see the source code of physics_aware_training, see the package in the physics_aware_training folder.

How to cite this code

If you use Physics-Aware Training in your research, please consider citing the following paper:

Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Tianyu Wang, Darren T. Schachter, Zoey Hu, and Peter L. McMahon (2021). Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems. https://arxiv.org/abs/2104.13386

License

The code in this repository is released under the following license:

Creative Commons Attribution 4.0 International

A copy of this license is given in this repository as license.txt.

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Comments

jupyter notebook syntax error

Error for syntax even thought the code is correct ( Editor used - Jupyter notebook ) code written : first_name = "Jack" print(first_name)

Error shown :

NameError Traceback (most recent call last) in 1 # Exercise 3 ----> 2 print(first_name)

NameError: name 'first_name' is not defined

opened by fearless0611 3
Questions
First of all, I want to thank you for the excellent documentation of the work. It's very impressive. Regarding to the repo and the paper, I have several questions listed below.

Regarding the machine learning task presented in the paper, are there any specific tasks that would be most appropriate for the specific physical systems, for example, for the SHG system? Did the same system show any limitations when trained for other ML tasks?

In the example code of the multilayer net for coupled pendula, I noticed that in the forward model the PNN is constructed by propagating the input through pendula 1, pendula 2, and pendula 3 using the same underlying neural networks. I was wondering why a three-times loop of one pendula is not used in In [30]? What if I would like to simulate a relatively deeper network, can I use a loop?

For the simulation of Coupled Pendulas and other tasks, I noticed that the user needs to define a way to extract the output in order to have a meaningful interpretation of the ML result. For instance, in Coupled Pendula, the output (final layer) is selected as to be the angle of the middle pendula. May I ask if this is a tricky part of training a PNN? Does the definition of the last layer matter a lot in terms of training? Do you have any suggestions on how I should define the last layer given a certain physical system?

Thanks for your time and patience!
opened by emilyxia 1
Could I establish my PNN on simulation platform such as PSCAD/EMTDC rather than a real circuit?

Hello! Thank you for your contributions in PNN. I am very excited to read this paper, which may be applied in my subject for fault diagnosis in power system. I want to establish a similar PNN on the simulation software like PSCAD/EMTDC first, or have ever implement PNN in some simulation software before?

opened by Jianhong-Gao 2
Documentation for The Actual Physical Setup?

Thank you all for this ground breaking work!! I really want to make my DIY PNN, but I ran into some questions after reading the docs. I'm wondering if you could publish a bit more documentations about the actual building process of the PNN?

For instance, in Example 1, the first oscillator network is composed of 196 oscillators. If I construct an audio-frequency mechanical oscillator from a commercially available speaker, just like you what did in the paper, does it mean that I would need to buy 196 speakers? And how would you connect them to form a "layer", and how would you make this layer coupled with the next layer?

I'm been working with digital NN only and I have some experience making some hobbyist breadboard circuits, but never in a scale like this. It would be a dream come true if I can make my own PNN..

Any help / directions / references would be appreciated!

opened by tjwangml 1

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Related tags

Overview

Getting started

How to cite this code

License

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Comments

jupyter notebook syntax error

Questions

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Documentation for The Actual Physical Setup?

Owner

McMahon Lab

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