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

Overview

g5382

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.

Example 1 animation

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

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.

Owner
McMahon Lab
McMahon Lab
Voxel Transformer for 3D object detection

Voxel Transformer This is a reproduced repo of Voxel Transformer for 3D object detection. The code is mainly based on OpenPCDet. Introduction We provi

173 Dec 25, 2022
Tree-based Search Graph for Approximate Nearest Neighbor Search

TBSG: Tree-based Search Graph for Approximate Nearest Neighbor Search. TBSG is a graph-based algorithm for ANNS based on Cover Tree, which is also an

Fanxbin 2 Dec 27, 2022
Code for the Higgs Boson Machine Learning Challenge organised by CERN & EPFL

A method to solve the Higgs boson challenge using Least Squares - Novae This project is the Project 1 of EPFL CS-433 Machine Learning. The project is

Giacomo Orsi 1 Nov 09, 2021
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

This dataset is a large-scale dataset for moving object detection and tracking in satellite videos, which consists of 40 satellite videos captured by Jilin-1 satellite platforms.

Qingyong 87 Dec 22, 2022
A Python package for faster, safer, and simpler ML processes

Bender 🤖 A Python package for faster, safer, and simpler ML processes. Why use bender? Bender will make your machine learning processes, faster, safe

Otovo 6 Dec 13, 2022
Rate-limit-semaphore - Semaphore implementation with rate limit restriction for async-style (any core)

Rate Limit Semaphore Rate limit semaphore for async-style (any core) There are t

Yan Kurbatov 4 Jun 21, 2022
Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models

Python scripts to detect faces using Python with the BlazeFace Tensorflow Lite models. Tested on Windows 10, Tensorflow 2.4.0 (Python 3.8).

Ibai Gorordo 46 Nov 17, 2022
Implementation of Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis

acLSTM_motion This folder contains an implementation of acRNN for the CMU motion database written in Pytorch. See the following links for more backgro

Yi_Zhou 61 Sep 07, 2022
Code for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss"

PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

Jinming Su 4 Aug 25, 2022
Repository for open research on optimizers.

Open Optimizers Repository for open research on optimizers. This is a test in sharing research/exploration as it happens. If you use anything from thi

Ariel Ekgren 6 Jun 24, 2022
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection Acknowledgement We implement our model, BtcDet, based on [OpenPcdet 0.3.0]. Insta

Qiangeng Xu 163 Dec 19, 2022
Python SDK for building, training, and deploying ML models

Overview of Kubeflow Fairing Kubeflow Fairing is a Python package that streamlines the process of building, training, and deploying machine learning (

Kubeflow 325 Dec 13, 2022
[ICLR'21] Counterfactual Generative Networks

This repository contains the code for the ICLR 2021 paper "Counterfactual Generative Networks" by Axel Sauer and Andreas Geiger. If you want to take the CGN for a spin and generate counterfactual ima

88 Jan 02, 2023
This program generates a random 12 digit/character password (upper and lowercase) and stores it in a file along with your username and app/website.

PasswordGeneratorAndVault This program generates a random 12 digit/character password (upper and lowercase) and stores it in a file along with your us

Chris 1 Feb 26, 2022
gACSON software for visualization, processing and analysis of three-dimensional electron microscopy images

gACSON gACSON software is to visualize, segment, and analyze the morphology of neurons in three-dimensional electron microscopy images. If you use any

Andrea Behanova 2 May 31, 2022
This is the official implementation of our proposed SwinMR

SwinMR This is the official implementation of our proposed SwinMR: Swin Transformer for Fast MRI Please cite: @article{huang2022swin, title={Swi

A Yang Lab (led by Dr Guang Yang) 27 Nov 17, 2022
use tensorflow 2.0 to tell a dog and cat from a specified picture

dog_or_cat use tensorflow 2.0 to tell a dog and cat from a specified picture This is one of the classic experiments for the introduction of deep learn

你这个代码我看不懂 1 Oct 22, 2021
code from "Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity"

Code associated with the paper "Tensor decomposition of higher-order correlations by nonlinear Hebbian learning," Ocker & Buice, Neurips 2021. "plot_f

Gabriel Koch Ocker 4 Oct 16, 2022
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

BossNAS This repository contains PyTorch evaluation code, retraining code and pretrained models of our paper: BossNAS: Exploring Hybrid CNN-transforme

Changlin Li 127 Dec 26, 2022
A simple pygame dino game which can also be trained and played by a NEAT KI

Dino Game AI Game The game itself was developed with the Pygame module pip install pygame You can also play it yourself by making the dino jump with t

Kilian Kier 7 Dec 05, 2022