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age-estimation-by-cnn

Age Estimation by Using a CNN (Convolutional Neural Network) Based Regression Model

auto-sklearn

Automated Machine Learning with scikit-learn

autoencoder

scripts in python for autoencoders using keras

autoencoders

Collection of autoencoder models in Tensorflow

autokeras

AutoML library for deep learning

awesome-matlab

A curated list of awesome Matlab frameworks, libraries and software.

bayesian_nn_ensembles

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

ccr_piezoresponse-force-microscopy-

Using CCR to predict piezoresponse force microscopy datasets

cluster-classify-regress-a-general-method-for-learning-discountinous-functions

cme-cnn

CME Arrival Time Prediction Using Convolutional Neural Network

conv-autoencoder

a convolutional autoencoder in python and keras.

cuqipy-demos

Repo for demo files and training materials

dafi

DAFI: Ensemble based data assimilation and field inversion, repository for internal development

dapper

Data Assimilation with Python: a Package for Experimental Research (DAPPER)

data_driven_mpc_controller_using_ccr

deepfield

Machine learning framework for reservoir simulation

deeplearningexamples

Deep Learning Examples

deeplearningimplementations

Implementation of recent Deep Learning papers

deepxde

Deep learning library for solving differential equations and more

devito

Code generation framework for automated finite difference computation

dlvkl

Deep latent-variable kernel learning

dmogpe

ensemble-based-history-matching-with-a-machine-learning-surrogate-reservoir-simulator

We have used a novel supervised learning, Cluster Classify Regress algorithm (CCR) for approximating 2 phase flow in a synthetic toy reservoir with very high accuracy. We compared the performance of CCR with a single DNN architecture in recovering the evolving pressure and saturation fields. The method consists of creating different surrogate machines equivalent to the number of time-steps (dynamic pressure and saturation snapshots). The inputs to the machine are the x,y,z spatial pixel (grid) location, the absolute permeability at each grid, effective porosity at each grid and the pressure and saturation field for each grid, for the previous time step. The outputs are the pressure and saturation field for the current time step Prediction is computationally cheap as each pressure and saturation map (for each time step) is recovered from each of the machines. The initial pressure and saturation field (Time 0) is fixed and set in the ECLIPSE data file. Learning of the function is first initiated by running eclipse once for the “1st time step” alone to get the preceding pressure and saturation field, CCR and DNN was then used to construct the different machines for each of the snap shots. CCR attained R2 accuracies of greater than 96% for both the recovery of the pressure and saturation field and Structural similarity index metric (SSIM) value of greater than 90% to the true pressure and saturation fields. We also use this newly constructed surrogate model in an ensemble based history matching frame-work. We show the overall frame work gives an acceptable history match (avoiding an inverse crime) to the synthetic true reservoir model. Finally we show the wall cock performance time of CCR in prediction (9.25 seconds on a standard personal laptop computer) compared to the full fidelity ECLIPSE reservoir solver to be 19.34 seconds. This is crucial in an ensemble based uncertainty quantification (UQ) task where the size of the ensemble ranges from 100 to 500 for full field reservoir history matching problems.

eql_tensorflow

Tensorflow implementation of equation learner

equation_learner

EQL Function Learning Network

ert

fastertransformer

Transformer related optimization, including BERT, GPT

fgp

Code for the paper 'Fast Allocation of Gaussian Process Experts'

fourier_neural_operator

Use Fourier transform to learn operators in differential equations.

gan-for-tabular-data

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.