1098994933 / Fatigue-life-prediction-of-aluminum-alloyLinks
☆25Updated 3 months ago
Alternatives and similar repositories for Fatigue-life-prediction-of-aluminum-alloy
Users that are interested in Fatigue-life-prediction-of-aluminum-alloy are comparing it to the libraries listed below
Sorting:
- A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materi…☆10Updated 2 years ago
- Extraction of mechanical properties of materials through deep learning from instrumented indentation☆68Updated 3 years ago
- This repository contains code for predicting multiaxial fatigue life of metals using deep learning models (CNN, LSTM, and GRU) combined w…☆19Updated last year
- Code base for the graph neural network-based polygrain microstructure property prediction project☆46Updated 3 years ago
- Machine learning model for complex concentrated alloys/high entropy alloys using TensorFlow☆15Updated 4 years ago
- Python scripts for physics-informed neural networks for corrosion-fatigue prognosis☆41Updated 3 years ago
- ☆12Updated 8 years ago
- Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Curre…☆12Updated 4 years ago
- ☆18Updated 4 years ago
- MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning☆35Updated 3 years ago
- This is the code for the paper 'Machine learning-enabled high-entropy alloy discovery'☆75Updated last year
- 采用PINN/ResPINN对两种偏微分方程(Burgers&Allen-Cahn)的训练与求解☆12Updated 4 years ago
- Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication☆17Updated 3 years ago
- Rheology-informed Machine Learning Projects☆20Updated last year
- Physics-Informed Neural Networks for Solving Multiscale Mode-Resolved Phonon Boltzmann Transport Equation☆22Updated 3 years ago
- A deep learning Bayesian framework for attribute driven inverse materials design☆14Updated 5 years ago
- Using machine learning to predict the performance of supercapacitors.☆11Updated 5 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆49Updated 3 years ago
- Thermodynamically Explainable Representations of AI and other black-box Paradigms☆34Updated 9 months ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆32Updated 3 years ago
- [MGE Advances 2025] Offical implement of BgoFace☆17Updated last week
- Predicting new perovskites with ensemble Machine Learning algorithms☆10Updated 4 years ago
- The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks☆38Updated 2 years ago
- Machine Learning for Catalyst Design and Discovery☆17Updated 6 years ago
- [NPJ Com Mat 2023 | Small 2024] Machine Learning Algorithm : outlier identifying, feature selection☆14Updated last week
- Physics-informed learning of governing equations from scarce data☆149Updated 2 years ago
- Use Abaqus FEA to determine the thermal conductivity of composite materials.☆13Updated 9 years ago
- MatDesign: a programming-free AI platform to predict and design materials☆71Updated 3 weeks ago
- ☆37Updated last year
- ☆20Updated last month