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 9 months ago
- 复现CICP论文提出的几种改进PINN性能的方法☆22Updated 5 months ago
- An improved and generic PINNs for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based …☆29Updated 2 years ago
- Python scripts for physics-informed neural networks for corrosion-fatigue prognosis☆41Updated 3 years ago
- The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks☆39Updated 3 years ago
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting☆38Updated 2 years ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆97Updated 2 years ago
- A pytorch implementation of several approaches using PINN to slove turbulent flow☆87Updated last year
- Scripts for the ANN publication submited to FEAD 2021☆14Updated 4 years ago
- This repo contains a PyTorch-based AE-ConvLSTM model for fluid flow prediction. It can forecast 5–10 time steps per forward pass and over…☆27Updated 6 months ago
- ☆131Updated 3 years ago
- ☆20Updated 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☆57Updated last year
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆35Updated 3 years ago
- ☆26Updated last year
- Implement PINN with high level APIs of TF2.0, including a solution of coupled PDEs with PINN☆27Updated 2 years ago
- Physics Informed Neural Networks: a starting step for CFD specialists☆37Updated 3 years ago
- Physics-guided Convolutional Neural Network☆68Updated 5 years ago
- Physics Informed Neural Network (PINN) for Burgers' equation.☆71Updated last year
- Extraction of mechanical properties of materials through deep learning from instrumented indentation☆72Updated 3 years ago
- A convolutional neural network for drag prediction in laminar flows☆15Updated 4 years ago
- ☆42Updated 2 years ago
- Physics-informed neural network for fatigue crack propagation (Paris' law)☆18Updated 3 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆51Updated 4 years ago
- A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations☆22Updated 3 years ago
- ☆13Updated 6 months ago
- Physics Informed Neural Network (PINN) for the 2D Navier-Stokes equation☆36Updated 3 years ago
- Examples implementing physics-informed neural networks (PINN) in Pytorch☆80Updated 4 years ago