Pages that link to "Item:Q831238"

The following pages link to A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Q831238):

Displaying 50 items.

• Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks (Q825483) (← links)
• Data-driven reduced order modeling for time-dependent problems (Q1986762) (← links)
• Error estimation of the parametric non-intrusive reduced order model using machine learning (Q1988235) (← links)
• Model reduction and neural networks for parametric PDEs (Q2050400) (← 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)
• A non-intrusive model order reduction approach for parameterized time-domain Maxwell's equations (Q2083308) (← links)
• Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains (Q2084084) (← links)
• A learning-based projection method for model order reduction of transport problems (Q2088794) (← links)
• Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition (Q2099719) (← links)
• Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs (Q2103427) (← links)
• B-DeepONet: an enhanced Bayesian deeponet for solving noisy parametric PDEs using accelerated replica exchange SGLD (Q2106911) (← links)
• Linear/ridge expansions: enhancing linear approximations by ridge functions (Q2124754) (← links)
• A long short-term memory embedding for hybrid uplifted reduced order models (Q2125587) (← links)
• Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning (Q2127404) (← links)
• Registration-based model reduction of parameterized two-dimensional conservation laws (Q2135818) (← links)
• The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations (Q2138717) (← links)
• A comparison of neural network architectures for data-driven reduced-order modeling (Q2138791) (← links)
• Model order reduction method based on (r)POD-ANNs for parameterized time-dependent partial differential equations (Q2158140) (← links)
• A non-intrusive neural network model order reduction algorithm for parameterized parabolic PDEs (Q2159858) (← links)
• Retracted: Model order reduction method based on machine learning for parameterized time-dependent partial differential equations (Q2161825) (← links)
• Machine learning for fast and reliable solution of time-dependent differential equations (Q2222523) (← links)
• Reduced-order deep learning for flow dynamics. The interplay between deep learning and model reduction (Q2222675) (← links)
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (Q2223001) (← links)
• Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines (Q2236543) (← links)
• A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications (Q2309058) (← links)
• A reduced order modeling method based on GNAT-embedded hybrid snapshot simulation (Q2672356) (← links)
• LaSDI: parametric latent space dynamics identification (Q2674132) (← links)
• Multi-fidelity surrogate modeling using long short-term memory networks (Q2678526) (← links)
• Operator inference for non-intrusive model reduction with quadratic manifolds (Q2679511) (← links)
• Accelerating algebraic multigrid methods via artificial neural networks (Q2679755) (← links)
• A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations (Q5005016) (← links)
• A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• The model reduction of the Vlasov–Poisson–Fokker–Planck system to the Poisson–Nernst–Planck system <i>via</i> the Deep Neural Network Approach (Q5163496) (← links)
• Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder (Q5886859) (← links)
• Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Q6048987) (← links)
• Neural-network-augmented projection-based model order reduction for mitigating the Kolmogorov barrier to reducibility (Q6054198) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Deep learning‐based reduced order models for the real‐time simulation of the nonlinear dynamics of microstructures (Q6071430) (← links)
• Preconditioned least‐squares Petrov–Galerkin reduced order models (Q6071434) (← links)
• Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)
• Non‐intrusive reduced‐order modeling using convolutional autoencoders (Q6092270) (← links)
• A non-intrusive data-driven reduced order model for parametrized CFD-DEM numerical simulations (Q6095091) (← links)
• POD-based reduced order methods for optimal control problems governed by parametric partial differential equation with varying boundary control (Q6096288) (← links)
• Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions (Q6097611) (← links)
• A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs (Q6097873) (← links)
• A dynamic mode decomposition based reduced-order model for parameterized time-dependent partial differential equations (Q6101657) (← links)
• Data assimilation predictive GAN (DA-PredGAN) applied to a spatio-temporal compartmental model in epidemiology (Q6101833) (← links)
• Front transport reduction for complex moving fronts (Q6111405) (← links)