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aeolis-python

A process-based model for simulating supply-limited aeolian sediment transport. This fork is focused on an in-progress implementation of sand fences and other coastal management interventions into Aeolis.

bending_stress_ann

bmi-cshore

bmi-example-python

An example of wrapping a model written in Python with a BMI

bmi-live

Code, docs, and Jupyter Notebooks for the BMI Live clinic at the CSDMS Annual Meeting

bmi-prms-demo

Repository contains notebooks to demo PRMS BMIs

bmi-prms6-surface

PRMS6 BMI for surface-zone including all processes above the soil-zone

cms2d

CMS is a coastal modeling system that couples a wave, circulation, and morphology model together to get better predictions in the near-shore.

cocos

on-the-fly estimation of coastal parameters from video of a wave field

computational_domain_pinns

cs229-2018-autumn

All notes and materials for the CS229: Machine Learning course by Stanford University

cshore

cshore

cshore-gui

d3d_add_turbines

MATLAB script for adding tidal turbines to Delft3D models as porous plates

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

decisiontree_waveforecasts

This is the live demo of the bagged regression tree paper.

duna

Matlab code of Duna model for wind-driven morphology change of coastal profiles

eqdiscovery

Physics-informed learning of governing equations from scarce data

equationdiscovery_mdmax_fdmax

funwave-tvd-withveg

fvcom-toolbox

A collection of Matlab post- and pre-processing tools for the Finite Volume Community Ocean Model (FVCOM)

gipl-bmi-fortran

This is to develop a BMI for GIPL with FORTRAN.

grainsizeanalysis-aeolis

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hybrid-pinn

hydraulic-modeling

jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

jax-cfd

Computational Fluid Dynamics in JAX

ml-road

Machine Learning Resources, Practice and Research