Pages that link to "Item:Q1986661"

The following pages link to Reduced order modeling for nonlinear structural analysis using Gaussian process regression (Q1986661):

Displaying 50 items.

• Data-driven POD-Galerkin reduced order model for turbulent flows (Q781977) (← links)
• ANOVA Gaussian process modeling for high-dimensional stochastic computational models (Q781984) (← links)
• Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks (Q825483) (← links)
• A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Q831238) (← links)
• Data-driven reduced order modeling for time-dependent problems (Q1986762) (← links)
• Model order reduction for large-scale structures with local nonlinearities (Q1988121) (← links)
• Error estimation of the parametric non-intrusive reduced order model using machine learning (Q1988235) (← links)
• Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling (Q1996002) (← links)
• Data-driven nonintrusive reduced order modeling for dynamical systems with moving boundaries using Gaussian process regression (Q2020804) (← links)
• Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics (Q2020980) (← links)
• Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
• An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques (Q2024169) (← links)
• Non-intrusive reduced-order modeling of parameterized electromagnetic scattering problems using cubic spline interpolation (Q2025869) (← links)
• Reduced-order modeling of nonlinear structural dynamical systems via element-wise stiffness evaluation procedure combined with hyper-reduction (Q2033650) (← links)
• Registration-based model reduction in complex two-dimensional geometries (Q2051104) (← links)
• POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (Q2060079) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: extension to geometrical parameterizations (Q2096859) (← links)
• Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs (Q2103427) (← links)
• A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
• Physics-informed machine learning for reduced-order modeling of nonlinear problems (Q2133556) (← links)
• A reduced order method for nonlinear parameterized partial differential equations using dynamic mode decomposition coupled with \(k\)-nearest-neighbors regression (Q2133585) (← links)
• Sequential Bayesian experimental design for estimation of extreme-event probability in stochastic input-to-response systems (Q2142170) (← links)
• A non-intrusive neural network model order reduction algorithm for parameterized parabolic PDEs (Q2159858) (← links)
• A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics (Q2167597) (← links)
• Model order reduction for compressible flows solved using the discontinuous Galerkin methods (Q2168284) (← links)
• Data-driven reduced order model with temporal convolutional neural network (Q2175300) (← links)
• A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems (Q2180467) (← links)
• Rare event simulation for large-scale structures with local nonlinearities (Q2184453) (← links)
• Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem (Q2214654) (← links)
• A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
• Adaptive non-intrusive reduced order modeling for compressible flows (Q2222527) (← links)
• Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion (Q2226314) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← links)
• Parametric non-intrusive model order reduction for flow-fields using unsupervised machine learning (Q2237497) (← links)
• A non-intrusive reduced-order modeling for uncertainty propagation of time-dependent problems using a B-splines Bézier elements-based method and proper orthogonal decomposition: application to dam-break flows (Q2239110) (← links)
• Efficient uncertainty quantification of CFD problems by combination of proper orthogonal decomposition and compressed sensing (Q2243373) (← links)
• A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
• A non-intrusive reduced basis EKI for time fractional diffusion inverse problems (Q2300550) (← links)
• Non-intrusive reduced-order modeling for uncertainty quantification of space-time-dependent parameterized problems (Q2656003) (← links)
• Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues (Q2678528) (← links)
• Parametric dynamic mode decomposition for reduced order modeling (Q2683069) (← links)
• 9 Kernel methods for surrogate modeling (Q3384280) (← links)
• A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems (Q5005000) (← links)
• Space-time registration-based model reduction of parameterized one-dimensional hyperbolic PDEs (Q5006302) (← links)
• A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
• (Q5129452) (← links)
• Non-Intrusive Reduced Order Modeling of Convection Dominated Flows Using Artificial Neural Networks with Application to Rayleigh-Taylor Instability (Q5163917) (← links)
• Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (Q5228359) (← links)
• Reduced basis methods for time-dependent problems (Q5887836) (← links)
• Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Q6048987) (← links)