SteliosTsop / QF-image-segmentation-kerasLinks
In this repository, we present an Semantic Segmentation code, based on U-net architecture, that is used for the topographic characterization of the fracture surfaces of brittle materials. The results of this work are presented in the publication: " Toward quantitative fractography using convolutional neural networks ".
☆15Updated 4 years ago
Alternatives and similar repositories for QF-image-segmentation-keras
Users that are interested in QF-image-segmentation-keras are comparing it to the libraries listed below
Sorting:
- CFD-DeepLearning-UNET☆17Updated 4 years ago
- This project aims at the problem of road surface image denoising and crack recognition by using embedded camera. Using Gaussian filter to…☆14Updated last year
- Benchmark problems of GPU computing of phase-field models☆13Updated 3 years ago
- Crack Analysis Tool in Python (CrackPy) - automatic detection and fracture mechanical analysis of (fatigue) cracks using digital image co…☆88Updated last year
- Adaptive phase field modeling of fracture using deep energy minimization.☆34Updated 4 years ago
- Different modeling techniques like multiple linear regression, decision tree, and random forest, etc. will be used for predicting the co…☆23Updated 5 years ago
- A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materi…☆10Updated 2 years ago
- Develop the time series recurrent neural network algorithms using LSTM and ARIMA in order 1) to forecast oil production of existing wells…☆13Updated 5 years ago
- Concrete Crack and Spalling Detection using Deep Neural Network☆88Updated 6 years ago
- Prediction and control of fracture paths in disordered architected materials using graph neural networks☆12Updated 2 years ago
- Predicting 2D Steady State Fluid Flow Fields using Convolutional Neural Networks☆10Updated 5 years ago
- Author's implementation of A deep convolutional neural network for rock fracture image segmentation☆28Updated 4 years ago
- Graph neural networks for stress and strain fields prediction☆55Updated 2 years ago
- ArunBaskaran / Image-Driven-Machine-Learning-Approach-for-Microstructure-Classification-and-Segmentation-Ti-6Al-4V☆20Updated 9 months ago
- Machine learning model for complex concentrated alloys/high entropy alloys using TensorFlow☆15Updated 4 years ago
- A Machine Learning approach to Finite Element Methods, using U-Net inspired architectures.☆29Updated 6 years ago
- GAN/convolutional and Transformer models to predict missing mechanical information given limited known data in part of the domain, and fu…☆20Updated 2 years ago
- A Finite Element Approximation of a Cahn--Hilliard Tumour Model with FEniCS, by Dennis Trautwein (2020).☆10Updated 5 years ago
- Application of Graph Neural Networks to predict material properties from their microstructures.☆18Updated 11 months ago
- Code to the Publication Meshfree Simulation of Metal Cutting: An Updated Lagrangian Approach with Dynamic Refinement☆13Updated 6 years ago
- Code base for the graph neural network-based polygrain microstructure property prediction project☆46Updated 3 years ago
- ☆10Updated 5 years ago
- Crack detection for concrete structure using Matlab☆32Updated 8 years ago
- ☆37Updated 7 years ago
- Incorporating Inductive Bias into Deep Learning: A Perspective from Automated Visual Inspection in Aircraft Maintenance☆57Updated 5 years ago
- TauFactor is a parallelised solver for calculating tortuosity factors from voxel data.☆32Updated 3 weeks ago
- Python scripts for physics-informed neural networks for corrosion-fatigue prognosis☆41Updated 3 years ago
- Physics-informed neural network for fatigue crack propagation (Paris' law)☆18Updated 3 years ago
- ☆13Updated 4 years ago
- A molecular dynamics tutorial for new researchers in the area of nanomechanics.☆15Updated 3 years ago