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This repository contains the Matlab and Python code for examples and exercises in the course TKT4108 Structural Dynamics 2 given at the Norwegian University of Science and Technology
Physics-informed deep learning for structural dynamics under moving load
Research/development of physics-informed neural networks for dynamic systems
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
ML-Ensemble – high performance ensemble learning
Ensemble learning related books, papers, videos, and toolboxes
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Must-read Papers on Physics-Informed Neural Networks.
A multi-feature fusion and any blending(stacking) learning framework based on scikit-learn.
LSTM-PINN and PINN for population forecasting
Flow field reconstruction and prediction of the 2D cylinder flow using data-driven physics-informed neural network combined with long short-term memory
An easy/swift-to-adapt PyTorch-Lighting template. 套壳模板，简单易用，稍改原来Pytorch代码，即可适配Lightning。You can translate your previous Pytorch code much easier using this template, and keep your freedom to edit a…
We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.
Data and code for paper "Tunnel deformation prediction during construction: An explainable hybrid model considering temporal and static factors" link:https://doi.org/10.1016/j.compstruc.2024.107276
A novel GNN-LSTM-based fusion model which could accurately predict the seismic responses of multiple structures with different geometry.
Recursive long short-term memory network for predicting nonlinear structural seismic response
Simulation of a SDOF Bouc-Wen-Baber-Noori hysteretic system
This a deep learning (Long Short-Term memory) multivariate time-series algorithm for forecasting of students' enrollment into courses
A Hybrid BiGRU-LSTM Neural Network for multivariate time-series prediction
Implementation of the TPA-LSTM model using Pytorch. This implementation is built for multivariate time series forecasting, but can easily be adapted for other purposes.
This project is an implementation of the paper Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. The model LSTNet consists of CNN, LSTM and RNN-skip layers
Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series (AAAI'20)
Implementation of Electric Load Forecasting Based on LSTM(BiLSTM). Including Univariate-SingleStep forecasting, Multivariate-SingleStep forecasting and Multivariate-MultiStep forecasting.