Pre-Processing, Post-Processing, and Visualization Tailored for OpenSeesPy

Flexible and efficient tools for high-dimensional approximation, scientific machine learning and uncertainty quantification.

The VECMA toolkit for creating surrogate models of multiscale systems.

LaTeX template for the response to reviewer comments (scientific journal publications)

Deep Gaussian Processes in matlab

open source code for the paper "Efficient Active Learning for Gaussian Process Classification by Error Reduction"

Visualization For OpenSees

古泉教授出版《OpenSees实用教程》算例

Probabilistic Estimation of Losses, Injuries, and Community resilience Under Natural hazard events

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

Decision making under uncertainty using the POMDPs.jl ecosystem taught by Robert Moss

Learning-Based Sequential Decision-Making Under Uncertainty

Decision making under uncertainty using the POMDPs.jl ecosystem taught by Robert Moss

Contour Location Via Entropy Reduction (NeurIPS 2018)

standard, parallel, constrained, and multiobjective EGO algorithms

Deep Reinforcement Learning

pyrelational is a python active learning library for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active le…

Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions…

MATLAB SVM toolbox written by Anton Schwaighofer

This is a more pythonic implementation of OpenSeesPy library to model and analyze structural problems in Jupyter notebooks

Some Python code that reads your opensees model's tcl files and shows you what it looks like

Helper function to boost OpenSees analyze.

Python Structural Reliability Analysis

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

A Neural Network implemented from scratch (using only numpy) in Python.