tongcezhou Goto Github PK
Name: T Zhou
Type: User
Name: T Zhou
Type: User
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Experiment code associated with our paper: "Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space"
Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: An Active Learning Approach
MIVor: An innovative adaptive Kriging approach for efficient problem classification.
Pytorch implementations of various types of autoencoders
Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.
Reusable BatchBALD implementation
Bayesian Experimental Design Coupling with Multi-fidelity Gaussian Processes (co-kriging) for Estimation of Hydraulic Conductivity in a Watershed
A Bayesian approach of the KPLS (Kriging using Partial Least Squares) method
Customized beamer templates based on SINTEF Presentation template
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
The code is used to solve structural reliability analysis problem via the BSC_RLCB method
The is the MATLAB code for calling ANSYS in MATLAB
Contour Location Via Entropy Reduction (NeurIPS 2018)
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"
Code examples in pyTorch and Tensorflow for CS230
DCEKit (Data Chemical Engineering toolKit)
Decision making under uncertainty using the POMDPs.jl ecosystem taught by Robert Moss
A repository that contains scripts to replicate results in the Deep UQ paper.
Deep Gaussian Processes in matlab
Designing a cantilevered beam under uncertainity using direct Monte Carlo simulation, FORM using HL-RF, and First Order Expansion
A Python 3 package for state-of-the-art statistical dimension reduction methods
Experiment code associated with our JMD paper: "Beyond the Known: Detecting Novel Feasible Domains over an Unbounded Design Space"
Codes related to our paper "Sparse Polynomial Chaos Expansions via D-optimal Designs and Compressed Sensing." https://www.sciencedirect.com/science/article/pii/S0045782518301452?via%3Dihub
MATLAB scripts for DPIM
The VECMA toolkit for creating surrogate models of multiscale systems.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 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. 📊📈🎉
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 ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.