Python Physical-informed Resources

Paperless-ngx Paperless-ngx is a document management system that transforms your physical documents into a searchable online archive so you can keep,

Sionna: An Open-Source Library for Next-Generation Physical Layer Research Sionna™ is an open-source Python library for link-level simulations of digi

Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction Figure 1. Teaser. Introduction This paper studies the problem

Finite basis physics-informed neural networks (FBPINNs) This repository reproduces the results of the paper Finite Basis Physics-Informed Neural Netwo

Intro Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" Robotics:Science and

PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street This is

Task-Informed Meta-Learning This repository contains examples of Task-Informed Meta-Learning (paper). We consider two tasks: Crop Type Classification

SBINN: Systems-biology informed neural network The source code for the paper M. Daneker, Z. Zhang, G. E. Karniadakis, & L. Lu. Systems biology: Identi

CoINN: Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels Accurate pressure drop estimat

ComPhy This repository holds the code for the paper. ComPhy: Compositional Physical Reasoning ofObjects and Events from Videos, (Under review) PDF Pro

The best way to use this material is by forking it by click the Fork button at the top, right corner. Then you will get your own copy to play with! Th

Pose Detection Project Description: Human pose estimation from video plays a critical role in various applications such as quantifying physical exerci

A Python application that helps users determine their calorie intake, and automatically generates customized weekly meal and workout plans based on metrics computed using their physical parameters

STARCH (Storm Tracking And Regional CHaracterization) STARCH computes regional extreme storm physical and moisture balance characteristics based on sp

Knowledge Informed Machine Learning using a Weibull-based Loss Function Exploring the concept of knowledge-informed machine learning with the use of a

DUQ is a python package for working with physical Dimensions, Units, and Quantities.

PhysConsts A Python package that provides physical constants. The code is being developed by Marc van der Sluys of the department of Astrophysics at t

Paperless-ng Paperless (click me) is an application by Daniel Quinn and contributors that indexes your scanned documents and allows you to easily sear

Overview This repository implements some common path planning algorithms used in robotics, including Search-based algorithms and Sampling-based algori

MixText This repo contains codes for the following paper: Jiaao Chen, Zichao Yang, Diyi Yang: MixText: Linguistically-Informed Interpolation of Hidden

Characterizing possible failure modes in physics-informed neural networks This repository contains the PyTorch source code for the experiments in the

POPPY: Physical Optics Propagation in Python POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propaga

Code Artifacts Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driv

Python package for analyzing sensor-collected human motion data

Domain-informed Neural Networks Codes for building and training the neural network model described in Domain-informed neural networks for interaction

SciANN: Neural Networks for Scientific Computations SciANN is a Keras wrapper for scientific computations and physics-informed deep learning. New to S

Notice: Support for Python 3.6 will be dropped in v.0.2.1, please plan accordingly! Efficient and Scalable Physics-Informed Deep Learning Collocation-

NeuralPDE NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using scientific machine learni

Physical Anomalous Trajectory or Motion (PHANTOM) Dataset Description This dataset contains the six different classes as described in our paper[]. The

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021) By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul,

Cryptick is a stock ticker for cryptocurrency tokens, and a physical NFT. This repository includes tools and documentation for the Cryptick device.

p0znMITM ARP Poisoning Tool What is ARP? Address Resolution Protocol (ARP) is a protocol or procedure that connects an ever-changing IP address to a f

Deep Latent Force Models This repository contains a PyTorch implementation of the deep latent force model (DLFM), presented in the paper, Compositiona

Official code of APHYNITY Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting (ICLR 2021, Oral) Yuan Yin*, Vincent Le Guen*

CRO-DeFi-Warner Intro CRO-DeFi-Warner can be used to notify you when a validator changes the commission rate or gets jailed. It can also notify you wh

Principled S2R Dehazing This repository contains the official implementation for PSD Framework introduced in the following paper: PSD: Principled Synt

FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack Case study of the FCA. The code can be find in FCA. Cas

LILA LILA: Language-Informed Latent Actions Code and Experiments for Language-Informed Latent Actions (LILA), for using natural language to guide assi

PINO Physics-informed Neural Operator for Learning Partial Differential Equation Abstract: Machine learning methods have recently shown promise in sol

RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network An official PyTorch implementation of the RBSRICNN network as desc

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Cascade Drone Swarm Physical Demonstration Project Table of Contents About The Project Built With Getting Started Prerequisites Installation About The

Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems Introduction Multi-agent control i

Physion: Evaluating Physical Prediction from Vision in Humans and Machines This repo contains code and data to reproduce the results in our paper, Phy

CDP - Self Council Data Project Council Data Project is an open-source project dedicated to providing journalists, activists, researchers, and all mem

Multi-Objective Loss Balancing for Physics-Informed Deep Learning Code for ReLoBRaLo. Abstract Physics Informed Neural Networks (PINN) are algorithms

IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N

DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a

API to fetch elements of the periodic table in JSON format. Uses Pandas for dumping .csv data to .json and Flask for API Integration. Deployed on "pyt

PhyCRNet Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs Paper link: [ArXiv] By: Pu Ren, Chengping Rao, Yang

PiSL A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery. Sun, F., Liu, Y. and Sun, H., 2021. Physics-informe

Phy-Q: A Benchmark for Physical Reasoning Cheng Xue*, Vimukthini Pinto*, Chathura Gamage* Ekaterina Nikonova, Peng Zhang, Jochen Renz School of Comput

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

Eviction Tracker Currently helping verify detainer warrant data for middle Tennessee - via Middle TN DSA - Red Door Collective Features Presents data

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

An Introduction to Deep Learning for the Physical Layer An usable PyTorch implementation of the noisy autoencoder infrastructure in the paper "An Intr

Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy

IDRLnet IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inver

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.