examples
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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