Bosh-Kuo / GNN-LSTM-based-Fusion-Model-for-Structural-Dynamic-Responses-PredictionLinks
A novel GNN-LSTM-based fusion model which could accurately predict the seismic responses of multiple structures with different geometry.
☆72Updated last year
Alternatives and similar repositories for GNN-LSTM-based-Fusion-Model-for-Structural-Dynamic-Responses-Prediction
Users that are interested in GNN-LSTM-based-Fusion-Model-for-Structural-Dynamic-Responses-Prediction are comparing it to the libraries listed below
Sorting:
- Recursive long short-term memory network for predicting nonlinear structural seismic response☆19Updated 3 years ago
- Physics-guided Convolutional Neural Network☆67Updated 4 years ago
- ☆9Updated last year
- Deep Learning for Structural Health monitoring☆16Updated 8 months ago
- Multivariate Time Series Forecasting with Graph Neural Networks☆13Updated 3 years ago
- Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks☆108Updated 2 years ago
- Air pollution prediction based on improved Informer model: a case study applied to the Yan'an city of China☆14Updated last year
- Physics-guided Neural Networks (PGNN) : An Application In Lake Temperature Modelling☆109Updated 3 years ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆90Updated last year
- ☆89Updated 3 years ago
- ResNet_LSTM_GCN☆13Updated 2 years ago
- A Framework for Remaining Useful Life Prediction Based on Self-Attention and Physics-Informed Neural Networks☆109Updated last year
- Industrial process, Silicon content, molten iron quality (MIQ) prediction, soft sensor, deep learning, sintering process, blast furnace i…☆27Updated 11 months ago
- This repository presents a series of analysis on the performance of Physics-Informed Neural Networks in vibrational systems. The limitati…☆12Updated 2 years ago
- A sequential combination of LSTM and MLP for the prediction of cutterhead torque for TBM.☆11Updated 4 years ago
- Repo allows users to test different DL archictectures when applied to time series forecasting of weather data (TCN, LSTM, BiLSTM, GRU, Bi…☆11Updated 3 months ago
- Time series forecasting especially in LSTF compare，include Informer, Autoformer, Reformer, Pyraformer, FEDformer, Transformer, MTGNN, LST…☆118Updated 2 years ago
- MVTS Classification with GCN-LSTM☆23Updated 3 years ago
- Short-term Air Quality Prediction Based on EMD-Transformer-BiLSTM☆36Updated last year
- A hybrid approach using physical information (PI) lightweight temporal convolutional neural networks (PI-TCN) for remaining useful life (…☆30Updated 2 years ago
- Ultra-short-term multi-step wind speed prediction for wind farms based on adaptive noise reduction technology and temporal convolutional …☆36Updated last year
- Reduced-order Variational Mode Decomposition (RVMD)☆27Updated 3 months ago
- use TCN and Transformer model for "Hourly Energy Consumption" data☆13Updated 3 years ago
- Deep LSTM for highly nonlinear system modeling☆56Updated 5 years ago
- Multi-Step Spatio-Temporal Forecasting: https://authors.elsevier.com/sd/article/S0306-2619(22)01822-0☆79Updated last year
- The relevant codes and more comparison results of "Physics-informed machine learning for tool wear monitoring".☆16Updated 3 years ago
- a multivariate time series deep spatiotemporal forecasting model with graph neural network (MDST-GNN) is proposed to solve the existing …☆33Updated 3 years ago
- This is a PyTorch implementation of STGAGRTN in the Spatial-Temporal Graph Attention Gated Recurrent Transformer Network for Traffic Flow…☆19Updated last year
- The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks☆36Updated 2 years ago
- Theory-guided deep-learning load forecasting is a short-term load forecasting model that combines domain knowledge and machine learning a…☆32Updated 3 years ago