mertstend / DigitalTwin_TutorialLinks
Digital twin for structural dynamics applications
☆23Updated 4 years ago
Alternatives and similar repositories for DigitalTwin_Tutorial
Users that are interested in DigitalTwin_Tutorial are comparing it to the libraries listed below
Sorting:
- In this repo we will show how to build a simple but useful Digital Twin using python. Our asset will be a Li-ion battery. This Digital Tw…☆93Updated 2 weeks ago
- Python scripts for physics-informed neural networks for corrosion-fatigue prognosis☆41Updated 3 years ago
- xaviergoby / ConvLSTM-Computer-Vision-for-Structural-Health-Monitoring-SHM-and-NonDestructive-Testing-NDTApplication of LSTM network for Structural Health Monitoring & Non-Destructive Testing☆42Updated 3 years ago
- Physics-informed learning of governing equations from scarce data☆12Updated 4 years ago
- SHMnet: Condition Assessment of Bolted Connection with Beyond Human-level Performance☆13Updated 5 years ago
- The NASA Prognostic Python Packages is a Python framework focused on defining and building models and algorit for prognostics (computatio…☆101Updated 2 months ago
- Project for dynamic identification and structural health monitoring of real structures using data-based statistical time series analysis☆17Updated last year
- This repository contains the scripts and preprocessed data to recreate the figures and results presented in the paper: A Comprehensive Re…☆36Updated 2 years ago
- CONCEPT is a dataset of lamb-wave measured in composite structures in healthy and damaged states. This experiment was conducted at the SH…☆27Updated 4 months ago
- Basic implementation of physics-informed neural networks for solving differential equations☆92Updated 8 months ago
- physics-guided neural networks (phygnn)☆95Updated 2 months ago
- Physics-informed neural networks package☆319Updated 3 years ago
- Digital twin with Python☆43Updated 3 years ago
- ☆130Updated 3 years ago
- Dashboard designed to demonstrate the power of Machine Learning to predict failures (Remaining Useful Life (RUL)) in wind turbines. To pr…☆49Updated 3 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆49Updated 2 years ago
- Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch☆43Updated 4 months ago
- Incorporating Inductive Bias into Deep Learning: A Perspective from Automated Visual Inspection in Aircraft Maintenance☆57Updated 5 years ago
- Code for the paper "Thermodynamics-informed graph neural networks" published in IEEE Transactions on Artificial Intelligence (TAI).☆102Updated last year
- Ansys Digital Twin repository☆30Updated this week
- Using adversarial training to improve forecasts of data-driven surrogate models☆17Updated 4 years ago
- Physics-Informed Neural Networks Trained with Particle Swarm Optimization☆21Updated 2 years ago
- The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining…☆126Updated last year
- Tutorials for Physics-Informed Neural Networks☆87Updated last year
- Introduction to Vibration Theory☆33Updated 3 months ago
- Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks☆111Updated 3 years ago
- An iterative machine learning framework for predicting temperature profiles for an additive manufacturing process☆37Updated 4 years ago
- Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems☆44Updated 2 years ago
- Different modeling techniques like multiple linear regression, decision tree, and random forest, etc. will be used for predicting the co…☆24Updated 5 years ago
- Predictive maintenance algorithm developed using digital twin of hydraulic pump modeled in Simscape☆36Updated 7 months ago