Pages that link to "Item:Q992149"
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The following pages link to Propagation of probabilistic uncertainty in complex physical systems using a stochastic finite element approach (Q992149):
Displaying 12 items.
- Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems (Q4610150) (← links)
- Bayesian adaptation of chaos representations using variational inference and sampling on geodesics (Q4626133) (← links)
- Stochastic finite element methods for partial differential equations with random input data (Q4683917) (← links)
- A stabilized finite element method for stochastic incompressible Navier–Stokes equations (Q4902866) (← links)
- A Generalized Polynomial Chaos-Based Method for Efficient Bayesian Calibration of Uncertain Computational Models (Q5244949) (← links)
- Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems (Q5306460) (← links)
- Design of linear parameter varying quadratic regulator in polynomial chaos framework (Q6054887) (← links)
- Space and chaos‐expansion Galerkin proper orthogonal decomposition low‐order discretization of partial differential equations for uncertainty quantification (Q6082495) (← links)
- Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate (Q6198152) (← links)
- Enhancing piecewise-defined surrogate response surfaces with adjoints on sets of unstructured samples to solve stochastic inverse problems (Q6553849) (← links)
- Propagating uncertainty through system dynamics in reproducing kernel Hilbert space (Q6554937) (← links)
- Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters (Q6663322) (← links)
