New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
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Domain-of-dependence stabilization for cut-cell meshes achieves fully discrete stability for linear advection under a CFL condition independent of arbitrarily small cell sizes.
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Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
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The domain-of-dependence stabilization for cut-cell meshes is fully discretely stable
Domain-of-dependence stabilization for cut-cell meshes achieves fully discrete stability for linear advection under a CFL condition independent of arbitrarily small cell sizes.