GitHub topics: bayesian-calibration
Plant-Food-Research-Open/calisim
A toolbox for the calibration and evaluation of simulation models.
Language: Python - Size: 7.15 MB - Last synced at: 30 days ago - Pushed at: about 1 month ago - Stars: 16 - Forks: 2
Plant-Food-Research-Open/calisim-examples-workshop-material
Workshop for calisim: A toolbox for the calibration and evaluation of simulation models.
Language: HTML - Size: 24.3 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 3 - Forks: 1
bandframework/surmise
A python package for surrogate models that interface with calibration and other tools
Language: Jupyter Notebook - Size: 195 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 13 - Forks: 7
NREL/PINNSTRIPES
Physics-Informed Neural Network SurrogaTe for Rapidly Identifying Parameters in Energy Systems
Language: Python - Size: 2.07 MB - Last synced at: 2 months ago - Pushed at: about 1 year ago - Stars: 45 - Forks: 18
TheseAdama/DOPEcal
Design of physical experiments for expensive computer code calibration
Language: R - Size: 110 KB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0
TheseAdama/DOEoptimizer
DOEoptimizer is a package that implements four optimization algorithms specifically designed for optimizing design of physical experiments criteria (a matrix input function).
Language: R - Size: 43.9 KB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0
jds485/RHESSys_ParamSA-Cal-GIOpt
Morris global sensitivity analysis, Bayesian DREAMzs calibration, and multi-objective optimization of green infrastructure using the RHESSys ecohydrological model.
Language: R - Size: 2.72 MB - Last synced at: 6 months ago - Pushed at: over 1 year ago - Stars: 5 - Forks: 2
hongyuanjia/epluspar
Conduct parametric analysis on EnergyPlus models in R
Language: R - Size: 694 KB - Last synced at: 8 months ago - Pushed at: over 1 year ago - Stars: 9 - Forks: 0
ideas-lab-nus/eplusr-paper
Data and code for Jia and Chong (2020): Hongyuan Jia and Adrian Chong (2020). eplusr: A framework for integrating building energy simulation and data-driven analytics. (Accepted in Energy and Buildings).
Size: 35.9 MB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 6 - Forks: 3
