stupid-cooh / Metal-Multiaxial-Fatigue-Life-Prediction-Using-Deep-LearningLinks
This repository contains code for predicting multiaxial fatigue life of metals using deep learning models (CNN, LSTM, and GRU) combined with fully connected layers. It processes a dataset published on Materials Cloud, utilizing high-quality data to train and evaluate the models effectively.
☆21Updated last year
Alternatives and similar repositories for Metal-Multiaxial-Fatigue-Life-Prediction-Using-Deep-Learning
Users that are interested in Metal-Multiaxial-Fatigue-Life-Prediction-Using-Deep-Learning are comparing it to the libraries listed below
Sorting:
- This repository contains the Python code for the paper "Transfer learning-based PINN model of 3D temperature field prediction for blue la…☆22Updated last year
- Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning☆26Updated 10 months ago
- Python scripts for physics-informed neural networks for corrosion-fatigue prognosis☆41Updated 3 years ago
- An improved and generic PINNs for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based …☆30Updated 2 years ago
- The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks☆41Updated 3 years ago
- 复现CICP论文提出的几种改进PINN性能的方法☆22Updated 5 months ago
- Scripts for the ANN publication submited to FEAD 2021☆14Updated 4 years ago
- Extraction of mechanical properties of materials through deep learning from instrumented indentation☆72Updated 3 years ago
- ☆21Updated 2 years ago
- Physics-informed neural network for fatigue crack propagation (Paris' law)☆20Updated 3 years ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆97Updated 2 years ago
- ☆27Updated last year
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting☆38Updated 2 years ago
- Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication☆19Updated 3 years ago
- Physics-guided Convolutional Neural Network☆68Updated 5 years ago
- This repository presents a series of analysis on the performance of Physics-Informed Neural Networks in vibrational systems. The limitati…☆13Updated 2 years ago
- Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks☆116Updated 3 years ago
- multi-fidelity neural network☆21Updated 2 years ago
- Physics-Informed Neural Network (PINN) for Solving Direct and Inverse Heat Conduction Problems☆13Updated 3 years ago
- A kind of loss function based on Least Squares Weighted Residual method for computational solid mechanics☆58Updated last year
- Recursive long short-term memory network for predicting nonlinear structural seismic response☆20Updated 4 years ago
- A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations☆23Updated 3 years ago
- A pytorch implementation of several approaches using PINN to slove turbulent flow☆89Updated last year
- Implement PINN with high level APIs of TF2.0, including a solution of coupled PDEs with PINN☆28Updated 2 years ago
- Multi-fidelity probability machine learning☆20Updated last month
- In recent years, the use of physics-informed neural networks (PINNs) has gained popularity across several engineering disciplines due to …☆11Updated last month
- Tensoflow 2 implementation of physics informed deep learning.☆27Updated 5 years ago
- MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning☆39Updated 3 years ago
- Bayesian PINN codes to solve 2D/3D Navier Stokes for wind fields☆10Updated 2 years ago
- ☆15Updated 6 months ago