Leeds Institute of Fluid Dynamics Machine Learning Notebooks
| Title: | Leeds Institute of Fluid Dynamics Machine Learning Notebooks |
|---|---|
| Authors: | Helen Burns; Matthew Gaddes; Oliver Pollard; Chetan Deva; Fergus Shone; Michael MacRaild; Phil Livermore; Leif Denby |
| Publisher Information: | Zenodo |
| Publication Year: | 2021 |
| Collection: | Zenodo |
| Subject Terms: | Machine Learning; Python; Tutorial; Jupyter Notebooks; Fluid Dynamics |
| Description: | Leeds Institute for Fluid Dynamics (LIFD) has teamed up with the Center for Environmental Modelling and Computation (CEMAC) team to create 4 Jupyter notebook tutorials on the following topics. ConvolutionalNeuralNetworks Physics_Informed_Neural_Networks GaussianProcesses RandomForests These notebooks require very little previous knowledge on a topic and will include links to further reading where necessary. Each Notebook should take about 2 hours to run through and should run out of the box home installations of Jupyter notebooks. How to Run These notebooks can run with the resources provided and the anaconda environment setup. If you are familiar with anaconda, juyter notebooks and GitHub. Simply clone this repository and run with in your Jupyter Notebook setup. Otherwise please read the how to run guide. Hardware These notebooks are designed to run on a personal computer. Although please note the techniques demonstrated can be very computationally intensive so there maybe options to skip steps depending on hardware available .e.g. use pre trained models. Knowledge No background knowledge is required on the environmental Science concepts or machine learning concepts. We have assumed some foundational knowledge but links are provided to indepth information on the fundamentals of each concept Future Releases These notebooks may be subject to minor updates following feedback from a wider audience |
| Document Type: | software |
| Language: | English |
| Relation: | https://github.com/cemac/LIFD_ENV_ML_NOTEBOOKS/tree/1.0-beta; https://cemac.github.io/LIFD_ENV_ML_NOTEBOOKS/; https://github.com/matthew-gaddes/VUDLNet_21; https://github.com/matthew-gaddes/VolcNet; https://github.com/matthew-gaddes/SyInterferoPy; https://distill.pub/2019/visual-exploration-gaussian-processes/; https://github.com/maziarraissi/PINNs; https://zenodo.org/communities/cemac/; https://zenodo.org/records/5227413; oai:zenodo.org:5227413; https://doi.org/10.5281/zenodo.5227413 |
| DOI: | 10.5281/zenodo.5227413 |
| Availability: | https://doi.org/10.5281/zenodo.5227413; https://zenodo.org/records/5227413 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: | edsbas.F482D47 |
| Database: | BASE |