cxb-cfd

cxb-cfd's Stars

• BIMK/PlatEMO

  Evolutionary multi-objective optimization platform

  Language:MATLAB1.6k463

• sandialabs/UQTk

  Sandia Uncertainty Quantification Toolkit

  Language:Fortran7026

• Xyce/Xyce

  The Xyce™ Parallel Electronic Simulator

  Language:C38458

• cossan-working-group/OpenCossan

  OpenCossan is an open and free toolbox for uncertainty quantification and management.

  Language:MATLAB4916

• uncertainty-toolbox/uncertainty-toolbox

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

  Language:Python1.8k129

• lorenzo-tamellini/sparse-grids-matlab-kit

  repository for the Sparse Grids Matlab Kit source code. Full info on https://sites.google.com/view/sparse-grids-kit

  Language:MATLAB32

• bishtgautam/matlab-script-for-clm-sparse-grid

  MATLAB scripts to create sparse grid surface dataset and domain file for E3SM Land Model and CLM

  Language:MATLAB410

• ABAtanasov/GalerkinSparseGrids.jl

  Sparse Grid Discretization with the Discontinuous Galerkin Method for solving PDEs

  Language:Julia4714

• SGpp/SGpp

  SG⁺⁺ – the numerical library for Sparse Grids in all their variants.

  Language:C++7035

• jonathf/chaospy

  Chaospy - Toolbox for performing uncertainty quantification.

  Language:Python43987

• maxehre/polynomial_surrogates

  Tools to construct surrogate models based on Hermitian polynomial bases. Includes full-factorial and sparse polynomial chaos expansions via least-angle regression as well as continuous-space low-rank approximations in canonical polyadics format.

  Language:Python107

• nasa/UQPCE

  Uncertainty Quantification using Polynomial Chaos Expansion (UQPCE) is an open source, python based research code for use in parametric, non-deterministic computational studies. UQPCE utilizes a non-intrusive polynomial chaos expansion surrogate modeling technique to efficiently estimate uncertainties for computational analyses. The software allows the user to perform an automated uncertainty analysis for any given computational code without requiring modification to the source. UQPCE estimates sensitivities, confidence intervals, and other model statistics, which can be useful in the conceptual design and analysis of flight vehicles. This software was developed for the Aeronautics Systems Analysis Branch (ASAB) within the Systems Analysis and Concepts Directorate (SACD) at NASA Langley Research Center to study potential impacts of uncertainties on the prediction of ground noise generated from commercial supersonic aircraft concepts.

  Language:Python279

• icdsigma-lee/Robust-Design-Optimization-under-Dependent-Random-Variables-by-a-Generalized-Polynomial-Chaos-Expans

  The paper was published in the Structural and Multidisciplinary Optimization Journal (Springer). The paper results of Examples 1-4 can be reproduced by the Matlab code files. To start the computation, run mainOpt.m file.

  Language:MATLAB7

• ShuaiGuo16/Explainable-UQ-Analysis

  Project source code and data for explainable machine-learning-based dimensionality reduction for fast uncertainty quantification.

  Language:MATLAB11

• ShuaiGuo16/Data-driven-High-dimensional-UQ-Analysis

  Project source code and data for uncertainty quantification on combustion instability prediction using a machine-learning-enhanced strategy

  Language:MATLAB81

• CrispDyt/gPC-Matlab-version

  A classical Uncertainty Quantification method - gPC

  Language:MATLAB11

• cxb-cfd/POD_DMD

  A tool to connect POD and DMD

  1

• HyokJungKim/AdaptiveSparseGrid

  Replication of Brumm & Scheidegger (2017, Econometrica)

  Language:C++11