Pages that link to "Item:Q2808259"

The following pages link to A survey of projection-based model reduction methods for parametric dynamical systems (Q2808259):

Displaying 50 items.

• Global sensitivity analysis for multivariate outputs using polynomial chaos-based surrogate models (Q2174747) (← links)
• Acceleration of the spectral stochastic FEM using POD and element based discrete empirical approximation for a micromechanical model of heterogeneous materials with random geometry (Q2175253) (← links)
• A nonlinear reduced order model with parametrized shape defects (Q2175315) (← links)
• Component-level proper orthogonal decomposition for flexible multibody systems (Q2176909) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations (Q2184449) (← links)
• Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (Q2190672) (← links)
• Model reduction of controlled Fokker-Planck and Liouville-von Neumann equations (Q2192448) (← links)
• Two-grid based adaptive proper orthogonal decomposition method for time dependent partial differential equations (Q2199697) (← links)
• An artificial neural network framework for reduced order modeling of transient flows (Q2206568) (← links)
• POD-(H)DG method for incompressible flow simulations (Q2210653) (← links)
• Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem (Q2214654) (← links)
• Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty (Q2214671) (← links)
• POD-based model order reduction with an adaptive snapshot selection for a discontinuous Galerkin approximation of the time-domain Maxwell's equations (Q2222407) (← links)
• Machine learning for fast and reliable solution of time-dependent differential equations (Q2222523) (← links)
• Adaptive non-intrusive reduced order modeling for compressible flows (Q2222527) (← links)
• Smoothing and parameter estimation by soft-adherence to governing equations (Q2222551) (← links)
• A nonintrusive reduced order modelling approach using proper orthogonal decomposition and locally adaptive sparse grids (Q2222606) (← links)
• Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion (Q2226314) (← links)
• Model order reduction of hyperbolic systems focusing on district heating networks (Q2235384) (← links)
• On-the-fly reduced order modeling of passive and reactive species via time-dependent manifolds (Q2237281) (← links)
• A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems (Q2237473) (← links)
• Reduced order modelling of nonlinear cross-diffusion systems (Q2242711) (← links)
• Reduced order modelling for a rotor-stator cavity using proper orthogonal decomposition (Q2245198) (← links)
• Projection-based and neural-net reduced order model for nonlinear Navier-Stokes equations (Q2245826) (← links)
• Windowed space-time least-squares Petrov-Galerkin model order reduction for nonlinear dynamical systems (Q2246254) (← links)
• Data-driven model order reduction for problems with parameter-dependent jump-discontinuities (Q2246398) (← links)
• Stability preservation in Galerkin-type projection-based model order reduction (Q2273097) (← links)
• Koopman operator-based model reduction for switched-system control of PDEs (Q2280764) (← links)
• Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction (Q2281470) (← links)
• Time domain model reduction of time-delay systems via orthogonal polynomial expansions (Q2287556) (← links)
• A randomized balanced proper orthogonal decomposition technique (Q2292020) (← links)
• Stochastic model order reduction in randomly parametered linear dynamical systems (Q2294802) (← links)
• Machine learning closures for model order reduction of thermal fluids (Q2295965) (← links)
• Proper orthogonal decomposition method for multiscale elliptic PDEs with random coefficients (Q2297081) (← links)
• Finite element model updating for structural applications (Q2297108) (← links)
• A non-intrusive reduced basis EKI for time fractional diffusion inverse problems (Q2300550) (← links)
• A bilinear \(\mathcal{H}_2\) model order reduction approach to linear parameter-varying systems (Q2305529) (← links)
• Kolmogorov \(n\)-widths for linear dynamical systems (Q2305530) (← links)
• A transport-based multifidelity preconditioner for Markov chain Monte Carlo (Q2305532) (← links)
• Analysis of parametric models. Linear methods and approximations (Q2305542) (← links)
• Randomized linear algebra for model reduction. I. Galerkin methods and error estimation (Q2305558) (← links)
• Data-driven operator inference for nonintrusive projection-based model reduction (Q2309194) (← links)
• Evaluation of Galerkin and Petrov-Galerkin model reduction for finite element approximations of the shallow water equations (Q2309775) (← links)
• A new reliability-based data-driven approach for noisy experimental data with physical constraints (Q2310160) (← links)
• A component-based hybrid reduced basis/finite element method for solid mechanics with local nonlinearities (Q2310185) (← links)
• A tensor decomposition algorithm for large ODEs with conservation laws (Q2324349) (← links)
• Finite-time balanced truncation for linear systems via shifted Legendre polynomials (Q2327360) (← links)
• A weighted POD method for elliptic PDEs with random inputs (Q2333690) (← links)
• A discrete Liouville identity for numerical reconstruction of Schrödinger potentials (Q2360779) (← links)