clementetienam Goto Github PK
Name: Clement Etienam
Type: User
Company: NVIDIA
Bio: Sr. Solution Architect @NVIDIA | Inverse Problems | Reservoir Simulation | AI Research Scientist | Physics Informed ML | Psalm 50 vs 15
Location: United Kingdom
Name: Clement Etienam
Type: User
Company: NVIDIA
Bio: Sr. Solution Architect @NVIDIA | Inverse Problems | Reservoir Simulation | AI Research Scientist | Physics Informed ML | Psalm 50 vs 15
Location: United Kingdom
Age Estimation by Using a CNN (Convolutional Neural Network) Based Regression Model
Automated Machine Learning with scikit-learn
scripts in python for autoencoders using keras
Collection of autoencoder models in Tensorflow
AutoML library for deep learning
A curated list of awesome Matlab frameworks, libraries and software.
AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
Using CCR to predict piezoresponse force microscopy datasets
CME Arrival Time Prediction Using Convolutional Neural Network
a convolutional autoencoder in python and keras.
Repo for demo files and training materials
DAFI: Ensemble based data assimilation and field inversion, repository for internal development
Data Assimilation with Python: a Package for Experimental Research (DAPPER)
Machine learning framework for reservoir simulation
Deep Learning Examples
Implementation of recent Deep Learning papers
Deep learning library for solving differential equations and more
Code generation framework for automated finite difference computation
Deep latent-variable kernel learning
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 β1st time stepβ 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.
Tensorflow implementation of equation learner
EQL Function Learning Network
Transformer related optimization, including BERT, GPT
Code for the paper 'Fast Allocation of Gaussian Process Experts'
Use Fourier transform to learn operators in differential equations.
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.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google β€οΈ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.