lijingwang

DataScienceForGeosciences

Data Science for the Geosciences

data_knowledge_driven_trend_surface

Stochastic geological surface modeling

IntroSpatialData_SDSI

Introduction to Spatial Data Analysis, Data Science Blog @ Stanford Data Science Institute

hierarchicalBayes

Open-source Python package for Hierarchical Bayesian inversion of global variables and large-scale spatial fields.

semi_supervised_seismic_interpretation

DGSA_Light

This is a light version of DGSA, written in Python

dssg_cv_tutorial

A tutorial session on convolutional neural network for Stanford Data Science for Social Good program

flopy

A Python package to create, run, and post-process MODFLOW-based models.

GEOLSCI-240-ENERGY-240

Data science for geoscience

lijingwang.github.io

Meta-Learning-Papers

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

Bayesian_inversion

clusterjob

ClusterJob: An automated system for painless and reproducible massive computational experiments

cme257-advanced-julia

Advanced Topics in Scientific Computing with Julia

cs230-code-examples

Code examples in pyTorch and Tensorflow for CS230

DataVizBlog

deep-residual-networks

Deep Residual Learning for Image Recognition

DGSA

DGSA-1

An R implementation of the DGSA method

DropoutUncertaintyExps

Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

dssg-reglab-public

fdagstat

An R package that implements the methods of geostatistics for functional data

fTree

An R package that implements methods for growing regression trees with functional and multivariate outputs

GS260_resources

Tutorials and resources for GS 260 Uncertainty Quantification in Subsurface Systems

Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

QUSS

Companion code for Scheidt, C, Li, L, and Caers, J. K. Quantifying Uncertainty in Subsurface Systems, John Wiley & Sons, 2017.

scikit-fmm

scikit-fmm is a Python extension module which implements the fast marching method.

SpatialAggregation_Capesmith

texture-synthesis

Texture synthesis in Torch

Tree-based_Direct_Sampling

This is the first version of the Tree-based Direct Sampling (TDS), with 2D Antactica Topography modeling case as example.