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3d-recurrent-r-u-net-for-co2-storage-with-geomechanics

aden

Bayesian Nonparametric Ensemble for Spatiotemporal Processes

afno-transformer

Adaptive FNO transformer - official Pytorch implementation

airfoil-gcnn

Graph Convolutional Networks for Unstructured Flow Fields

altimeter_read

Read satellite altimetry data of Netcdf format

assessing-pinns-ocean-modelling

Code and datasets for paper "Assessing Weighted Physics Informed Neural Networks in Ocean Modelling"

atmds

This repository is under construction. It will be used to store all necessary function for the downscaling of atmspheric forcing variables.

basic-pinn

Basic implementation of physics-informed neural networks for solving differential equations

bathymetry

Project on Bathymetry (Water Depth) Mapping using LandSat satellite imagery

bayesian-hydrological-modeling

Mini Project on Bayesian treatment of hydrological models

bayesian-pinn-pytorch

bayesian_nn_ensembles

AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'

bayesian_optimization_deep_learning

optimized parameters of deep learning using Bayesian optimization

bayesian_uncertainty_lstm

Bayesian, Uncertainty, Neutral Networks, LSTM, time series

bayesian_unet

Chainer implementation of Bayesian Convolutional Neural Networks (BCNNs)

bayesianlstm

Bayesian LSTM (Tensorflow)

bayesianneuralnet_stockmarket

Bayesian neural network with Parallel Tempering MCMC for Stock Market Prediction

bcnn4grace

A Bayesian Convolutional Neural Network for reconstructing GRACE TWSA signals

bias_correction

python library for bias correction

bioclima_calabria

causalpinns

ccsnet_v1.0

CCSNet: a deep learning modeling suite for CO2 storage

chaospy

Chaospy - Toolbox for performing uncertainty quantification.

cikm-cup-2017

CIKM AnalytiCup 2017: Short-Term Precipitation Forecasting Based on Radar Reflectivity Images

cnn-surrogate

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

cnn_l8_s2

cnn_lstm_era

Code used in the study "Evaluation and interpretation of convolutional long-short term memory networks for regional hydrological modelling"

cnn_lstm_interpret

Code for the project "Interpreting deep machine learning for streamflow modelling across glacial, nival, and pluvial regimes in southwestern Canada".

codes-of-tead

This is the codes and examples described in "Mo, S., Lu, D., Shi, X., Zhang, G., Ye, M., Wu, J., & Wu, J. (2017). A Taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling. Water Resources Research, 53, 10,802–10,823. https://doi.org/10.1002/2017WR021622"

dambreaksimulation

Implementation of 2d finite volume method in MatLab