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resspylab

Resilient Steel Structures Laboratory (RESSLab) Python Library

salib

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

satut

Tutorials for Sensitivity Analysis using SALib

scf

传统自复位框架

seismic-hazard-and-risk

Supporting calculations for the textbook Seismic Hazard and Risk Analysis

self_transfer

sr-ann-models-balkans

This repository contains artificial neural network models ,developed in Python using the Tensorflow library, for the estimation of structural response, damage and loss of common building typologies in the Balkan region.

srbench

A living benchmark framework for symbolic regression

stanford---machine-learning-coursework

Topics - Linear Regression, Logistic Regression, Regularization, Neural Networks, System Design, Support Vector Machines, Unsupervised Learning (k-Means algorithm for clustering), Dimensionality Reduction (principal components analysis), Anomaly Detection, Recommender Systems, Large Scale Machine Learning, and Photo Optical Character Recognition.

stat-learning

Notes and exercise attempts for "An Introduction to Statistical Learning"

stateoftheartadaptivesampling

stk

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, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.

stochastic_seismic_generator

A C++ software useful for generating the seismic ground motion with inputs of Distance, fault dimensions, orientation and soil profile on origin and site

structgnn

StructGNN: An Efficient Graph Neural Network Framework for Static Structural Analysis

structural-model-updating

This GitHub package provides example MATLAB code for finite element model updating. The code offers selection of different updating formulations and optimization algorithms.

surrogatemodel

This repository contains the software developed for the implementation of surrogate models on dynamical systems, a technique that is thoroughly developed in the paper 'Surrogate Models for Optimization of Dynamical Systems' to appear in “Foundations of Modern Statistics“

teds-toolboxengineeringdesignsensitivity

A sensitivity toolbox that is tailored to the design process in the presence of uncertainties

time-series-prediction-narx

The project uses a nonlinear autoregressive exogenous (NARX), model to make time-series prediction on data obtained from drive cycling testing on buses

uq-course

Introduction to Uncertainty Quantification

usnccm15-short-course-recent-advances-in-physics-informed-deep-learning

windloading

PhD project: A multi-fidelity numerical framework to predict wind loads on buildings