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

{"references": ["Matthew Gaddes et al 2021,Simultaneous classification and location of volcanic deformation in SAR interferograms using deep learning and the VolcNet database. https://doi.org/10.31223/X5CW2J", "Gaddes, M. E., Hooper, A., & Bagnardi, M. (2019). Using machine learning to automatically detect volcanic unrest in a time series of interferograms. Journal of Geophysical Research: Solid Earth, 124, 12304\u2013 12322. https://doi.org/10.1029/2019JB017519", "G\u00f6rtler, et al., \"A Visual Exploration of Gaussian Processes\", Distill, 2019.", "M. Raissi et al.Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics.https://doi.org/10.1016/j.jcp.2018.10.045."]}