This repo contains the code required to train the multivariate time-series Transformer.

Last update: Nov 24, 2022

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Overview

Multi-Variate Time-Series Transformer

This repo contains the code required to train the multivariate time-series Transformer.

Download the data

The Non-Homogeneous Compound Poisson Process aeroelastic simulation data can be downloaded from: https://doi.org/10.5281/zenodo.5544042

Usage

First modify the yaml config file as desired, then the training may be launched with:

python3 train.py -c path/to/config.yaml

Cite

Duthé, G., Abdallah, I., Barber, S., & Chatzi, E. (2021). Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. Energies, 14(21), 7262. https://doi.org/10.3390/en14217262

Requirements

einops>=0.3.0
h5py>=2.10.0
numpy>=1.20.1
python_box>=5.3.0
PyYAML>=6.0
torch>=1.8.0
tqdm>=4.60.0

Owner

Gregory Duthé

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This repo contains the code required to train the multivariate time-series Transformer.

Related tags

Overview

Multi-Variate Time-Series Transformer

Download the data

Usage

Cite

Requirements

Owner

Gregory Duthé

Solver for Large-Scale Rank-One Semidefinite Relaxations

FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)

Py-FEAT: Python Facial Expression Analysis Toolbox

Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

Visualizer using audio and semantic analysis to explore BigGAN (Brock et al., 2018) latent space.

Java and SHACL code commented in the paper "Towards compliance checking in reified I/O logic via SHACL" submitted to ICAIL 2021

Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Scheme for training and applying a label propagation framework

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