./examples/Modeling/FlowEquation/python/DarcyFlow.ipynb: "MUQ implements components of models through the ModPiece. Readers that are not familiar with the ModPiece class should consult the model compoents tutorial before proceeding. \n",
./examples/Modeling/FlowEquation/python/DarcyFlow.ipynb: "We can now set up the eigen solver, compute the decomposition, and extract the eigenvalues and eigenvectors. For more information, see documentation for the StochasticEigenSolver class."
./examples/Modeling/FlowEquation/cpp/FlowEquation.cpp:MUQ implements components of models through the ModPiece. Readers that are not familiar with the ModPiece class should consult the model compoents tutorial before proceeding.
./examples/Modeling/FlowEquation/cpp/FlowEquation.cpp:information, see documentation for the StochasticEigenSolver class.
./examples/SamplingAlgorithms/MCMC/MultilevelMCMC_FlowModel/cpp/FlowEquation.cpp:MUQ implements components of models through the ModPiece. Readers that are not familiar with the ModPiece class should consult the model compoents tutorial before proceeding.
./examples/SamplingAlgorithms/MCMC/MultilevelMCMC_FlowModel/cpp/FlowEquation.cpp:information, see documentation for the StochasticEigenSolver class.
./examples/SamplingAlgorithms/MCMC/EllipticInference/python/DILI/EllipticInference.ipynb: "For comparison with the DILI results, we will use a standard preconditioned Crank-Nicolson proposal. This method is discretization invariant, but does not leverage the same structure as DILI and has much poorer performance. Other geometry-aware enhancements of the pCN proposal are also available in MUQ (e.g., $\infty$-MALA), but are not shown in this example."
./examples/SamplingAlgorithms/MCMC/EllipticInference/cpp/InvariantSampling.cpp:For comparison with the DILI results, we will use a standard preconditioned Crank-Nicolson proposal. This method is discretization invariant, but does not leverage the same structure as DILI and has much poorer performance. Other geometry-aware enhancements of the pCN proposal are also available in MUQ (e.g., $\infty$-MALA), but are not shown in this example.
./examples/Modeling/FlowEquation/python/DarcyFlow.ipynb: "MUQ implements components of models through the
ModPiece. Readers that are not familiar with theModPiececlass should consult the model compoents tutorial before proceeding. \n", ./examples/Modeling/FlowEquation/python/DarcyFlow.ipynb: "We can now set up the eigen solver, compute the decomposition, and extract the eigenvalues and eigenvectors. For more information, see documentation for the StochasticEigenSolver class." ./examples/Modeling/FlowEquation/cpp/FlowEquation.cpp:MUQ implements components of models through theModPiece. Readers that are not familiar with theModPiececlass should consult the model compoents tutorial before proceeding. ./examples/Modeling/FlowEquation/cpp/FlowEquation.cpp:information, see documentation for the StochasticEigenSolver class. ./examples/SamplingAlgorithms/MCMC/MultilevelMCMC_FlowModel/cpp/FlowEquation.cpp:MUQ implements components of models through theModPiece. Readers that are not familiar with theModPiececlass should consult the model compoents tutorial before proceeding. ./examples/SamplingAlgorithms/MCMC/MultilevelMCMC_FlowModel/cpp/FlowEquation.cpp:information, see documentation for the StochasticEigenSolver class. ./examples/SamplingAlgorithms/MCMC/EllipticInference/python/DILI/EllipticInference.ipynb: "For comparison with the DILI results, we will use a standard preconditioned Crank-Nicolson proposal. This method is discretization invariant, but does not leverage the same structure as DILI and has much poorer performance. Other geometry-aware enhancements of the pCN proposal are also available in MUQ (e.g., $\infty$-MALA), but are not shown in this example." ./examples/SamplingAlgorithms/MCMC/EllipticInference/cpp/InvariantSampling.cpp:For comparison with the DILI results, we will use a standard preconditioned Crank-Nicolson proposal. This method is discretization invariant, but does not leverage the same structure as DILI and has much poorer performance. Other geometry-aware enhancements of the pCN proposal are also available in MUQ (e.g., $\infty$-MALA), but are not shown in this example.