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README
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README
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EXAMPLES
==============================
d_spring_series
==============================
Example with springs in series to demonstrate dimensionality
reduction through basis adaptation (Xiaoshu Zeng, Aug 2018)
==============================
heat_transfer_window
==============================
forward propagation with heat transfer example
==============================
kle_ex1
==============================
Karhunen-Loeve expansion example
==============================
line_infer
==============================
Example to calibrate parameters of a linear models
==============================
muq
==============================
inference between MUQ and UQTk
==============================
num_integ
==============================
Examples on quadrature and Monte Carlo integrations
==============================
ops
==============================
operations with Polynomial Chaos expansions
==============================
pce_bcs
==============================
Example to construct sparse Polynomial Chaos expansions
==============================
polynomial
==============================
Example of polynomial model fit with MCMC
==============================
sensMC
==============================
Monte-Carlo based sensitivity index computation
==============================
surf_rxn
==============================
surface reaction example for forward and inverse UQ
==============================
uqpc
==============================
construct Polynomial Chaos surrogates for multiple outputs/functions
==============================
um_bridge
==============================
Uses C++ TMCMC to sample from multimodal posterior, and um-bridge
to evaluate samples from a python function
==============================
iuq
==============================
surrogate-enabled inverse UQ workflow
==============================
tmcmc_bimodal
==============================
use TMCMC to sample from a 3-dimensional posterior that is a
product of a Gaussian prior and a bimodal likelihood
==============================
dfi
==============================
use data-free inference to recover an approximate correlated posterior distribution for parameters of a line model
given summary statistics on marginals
==============================
dfi_app
==============================
use the dfi app to calibrate a quadratic model given summary statistics on the pushforward posterior