Pinned Repositories
100-Days-Of-ML-Code
100-Days-Of-ML-Code中文版
A-polynomial-regression-machine-learning-approach-for-reliability-based-optimization
This is a compact code for reliability analysis under uncertainty using a Polynomial Regression Machine Learning approach. The code implements a stochastic response surface method (SRSM) which quantifies the uncertainty in a performance function for the purpose of reliability-based design optimization (RBDO).
Active-Subspace_Paul-G.-Constantine
AeroSandbox
Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
AMK-MCS-AEFF
an active-learning method for reliability analysis based on multi-fidelity kriging model
aPCE
Matlab codes for Arbitrary Polynomial Chaos Expansion
Applied-Deep-Learning
Applied Deep Learning Course
AutoSDAPlatform
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
python_structural_dynamics
Python and Jupyter notebooks about Fundamentals of Engineering Structural Dynamics.
chengsfsu's Repositories
chengsfsu/AeroSandbox
Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
chengsfsu/AMK-MCS-AEFF
an active-learning method for reliability analysis based on multi-fidelity kriging model
chengsfsu/Applied-Deep-Learning
Applied Deep Learning Course
chengsfsu/bayesoptbook.github.io
Companion webpage for the book "Bayesian Optimization" by Roman Garnett
chengsfsu/Data_Driven_Science_Python_Demos
IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by J. Nathan Kutz and Steven L. Brunton
chengsfsu/deep_euler_tests
Codes for testing the Deep Euler Method
chengsfsu/ditto
Eclipse Ditto™ Project
chengsfsu/dreaminsg-integrated-model
An integrated water-power-transportation model for network simulations.
chengsfsu/GNN-LSTM-based-Fusion-Model-for-Structural-Dynamic-Responses-Prediction
A novel GNN-LSTM-based fusion model which could accurately predict the seismic responses of multiple structures with different geometry.
chengsfsu/gpax
Structured Gaussian Processes and Deep Kernel Learning
chengsfsu/GroundMotionUtilities
GMU - A set of widgets and applications for ground motions
chengsfsu/Incremental_Kriging_Assisted_Evolutionary_Algorithm
A fast Kriging-assisted evolutionary algorithm based on incremental learning
chengsfsu/little-book-of-dl
Clearly explained notes of everything in The Little Book of Deep Learning.
chengsfsu/MultiHazardEventSetSimulation
The following codes are used for implementing the algorithm presented in Iannacone et al. (2023). Simulating multi-hazard event sets for life cycle consequence analysis
chengsfsu/NeuralABM
Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks
chengsfsu/nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
chengsfsu/Open-Vibrations
An open-source textbook intended to cover the basics of mechanical vibrations. This text is intended to function as the only text required for a college (undergraduate) class on vibrations.
chengsfsu/pdhi
Physics-DNN hybridized integration time stepper (demo)
chengsfsu/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
chengsfsu/Reinforcement-Learning-for-Active-Structural-Control
chengsfsu/RK4_PINN
chengsfsu/SALib
Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
chengsfsu/seismic-hazard-and-risk
Supporting calculations for the textbook Seismic Hazard and Risk Analysis
chengsfsu/Self_transfer
chengsfsu/srbench
A living benchmark framework for symbolic regression
chengsfsu/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.
chengsfsu/StructGNN
StructGNN: An Efficient Graph Neural Network Framework for Static Structural Analysis
chengsfsu/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.
chengsfsu/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“
chengsfsu/TEDS-ToolboxEngineeringDesignSensitivity
A sensitivity toolbox that is tailored to the design process in the presence of uncertainties