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.

• Entropy-based convergence rates of greedy algorithms (Q6551364) (← links)
• Adaptive reduced order modeling for nonlinear dynamical systems through a new a posteriori error estimator: application to uncertainty quantification (Q6553492) (← links)
• Information theoretic clustering for coarse-grained modeling of non-equilibrium gas dynamics (Q6553796) (← links)
• A greedy reduced basis algorithm for structural acoustic systems with parameter and implicit frequency dependence (Q6554065) (← links)
• An adaptive sparse grid rational Arnoldi method for uncertainty quantification of dynamical systems in the frequency domain (Q6554128) (← links)
• Adaptive greedy algorithms based on parameter-domain decomposition and reconstruction for the reduced basis method (Q6554352) (← links)
• Generalized quadratic embeddings for nonlinear dynamics using deep learning (Q6554923) (← links)
• Transported snapshot model order reduction approach for parametric, steady-state fluid flows containing parameter-dependent shocks (Q6555323) (← links)
• A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems (Q6555345) (← links)
• A POD-selective inverse distance weighting method for fast parametrized shape morphing (Q6555410) (← links)
• Improved model reduction with basis enrichment for dynamic analysis of nearly periodic structures including substructures with geometric changes (Q6556769) (← links)
• Error modeling for surrogates of dynamical systems using machine learning (Q6557511) (← links)
• Gradient preserving operator inference: data-driven reduced-order models for equations with gradient structure (Q6557793) (← links)
• An adaptive model order reduction technique for parameter-dependent modular structures (Q6558965) (← links)
• Deep learning enhanced dynamic mode decomposition (Q6560590) (← links)
• Computation of controllability and observability gramians in modeling of discrete-time noncommensurate fractional-order systems (Q6563424) (← links)
• Multivariate predictions of local reduced-order-model errors and dimensions (Q6565197) (← links)
• On the sample complexity of stabilizing linear dynamical systems from data (Q6566152) (← links)
• Stability preserving data-driven models with latent dynamics (Q6567566) (← links)
• Reduced order modeling of random linear dynamical systems based on a new a posteriori error bound (Q6569301) (← links)
• Model order reduction based on low-rank decomposition of the cross Gramian (Q6575259) (← links)
• Laguerre-based parametric order reduction for parametric systems by multi-order Arnoldi (Q6579054) (← links)
• Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models (Q6581233) (← links)
• Lagrangian descriptors with uncertainty (Q6584212) (← links)
• Grassmannian kriging with applications in POD-based model order reduction (Q6584836) (← links)
• When data driven reduced order modeling meets full waveform inversion (Q6585280) (← links)
• Real-time computing for a parameterized feedback control problem of Boussinesq equations by POD and deep learning (Q6586295) (← links)
• Identification of dominant subspaces for model reduction of structured parametric systems (Q6589325) (← links)
• Learning-based multi-continuum model for multiscale flow problems (Q6589888) (← links)
• An augmented subspace based adaptive proper orthogonal decomposition method for time dependent partial differential equations (Q6589899) (← links)
• Partially explicit splitting scheme with explicit-implicit-null method for nonlinear multiscale flow problems (Q6591757) (← links)
• Model Reduction of Parametric Differential-Algebraic Systems by Balanced Truncation (Q6593072) (← links)
• A mass-conservative reduced-order algorithm in solving optimal control of convection-diffusion equation (Q6594663) (← links)
• Operator inference driven data assimilation for high fidelity neutron transport (Q6595882) (← links)
• A hybrid numerical methodology coupling reduced order modeling and graph neural networks for non-parametric geometries: applications to structural dynamics problems (Q6595908) (← links)
• Optimal experimental design: formulations and computations (Q6598420) (← links)
• Randomized greedy magic point selection schemes for nonlinear model reduction (Q6601294) (← links)
• Modern Monte Carlo methods for efficient uncertainty quantification and propagation: a survey (Q6602125) (← links)
• Time-limited pseudo-optimal \(\mathscr{H}_2\)-model order reduction (Q6608935) (← links)
• A hyperreduced reduced basis element method for reduced-order modeling of component-based nonlinear systems (Q6609763) (← links)
• Derivation of geometrically parameterized shell elements in the context of shape optimization (Q6622362) (← links)
• A reduced conjugate gradient basis method for fractional diffusion (Q6623676) (← links)
• Machine learning methods for reduced order modeling (Q6629175) (← links)
• Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions (Q6631167) (← links)
• Domain decomposition for physics-data combined neural network based parametric reduced order modelling (Q6639365) (← links)
• Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning (Q6652579) (← links)
• Application of deep learning reduced-order modeling for single-phase flow in faulted porous media (Q6662482) (← links)
• Structured optimization-based model order reduction for parametric systems (Q6663230) (← links)
• Fully adaptive structure-preserving hyper-reduction of parametric Hamiltonian systems (Q6663234) (← links)
• System stabilization with policy optimization on unstable latent manifolds (Q6663289) (← links)