Pages that link to "Item:Q2180467"

The following pages link to A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems (Q2180467):

Displaying 28 items.

• Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction (Q725460) (← links)
• Reduced order modeling for nonlinear structural analysis using Gaussian process regression (Q1986661) (← links)
• Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling (Q1996002) (← links)
• Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
• POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (Q2060079) (← links)
• Projection-based model reduction of dynamical systems using space-time subspace and machine learning (Q2072456) (← links)
• Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (Q2072477) (← links)
• A non-intrusive model order reduction approach for parameterized time-domain Maxwell's equations (Q2083308) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: extension to geometrical parameterizations (Q2096859) (← links)
• Non intrusive method for parametric model order reduction using a bi-calibrated interpolation on the Grassmann manifold (Q2126992) (← links)
• Registration-based model reduction of parameterized two-dimensional conservation laws (Q2135818) (← links)
• Adaptive non-intrusive reduced order modeling for compressible flows (Q2222527) (← links)
• A variable-fidelity hybrid surrogate approach for quantifying uncertainties in the nonlinear response of braided composites (Q2237005) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← links)
• Model order reduction of nonlinear homogenization problems using a Hashin-Shtrikman type finite element method (Q2310200) (← links)
• Inverse analysis method using MPP-based dimension reduction for reliability-based design optimization of nonlinear and multi-dimensional systems (Q2638108) (← links)
• Non-intrusive reduced-order modeling for uncertainty quantification of space-time-dependent parameterized problems (Q2656003) (← links)
• The adjoint method coupled with the modal identification method for nonlinear model reduction (Q3588273) (← links)
• Nondestructive evaluation using a reduced-order computational methodology (Q4507839) (← links)
• Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Q6048987) (← links)
• A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods (Q6060947) (← links)
• An iterative multi‐fidelity approach for model order reduction of multidimensional input parametric PDE systems (Q6082635) (← links)
• An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations (Q6110109) (← links)
• Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots (Q6119273) (← links)
• Model order reduction for parameterized electromagnetic problems using matrix decomposition and deep neural networks (Q6137793) (← links)
• Active-learning-driven surrogate modeling for efficient simulation of parametric nonlinear systems (Q6185211) (← links)
• Registration-based model reduction of parameterized PDEs with spatio-parameter adaptivity (Q6196631) (← links)
• GFN: a graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications (Q6643617) (← links)