Pages that link to "Item:Q2127404"

The following pages link to Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning (Q2127404):

Displaying 28 items.

• Applied unsupervised learning in model reduction of linear dynamic systems (Q679283) (← links)
• Neural network closures for nonlinear model order reduction (Q1756917) (← links)
• Fast reduced model of non-linear dynamic Euler-Bernoulli beam behaviour (Q1953608) (← 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)
• Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms (Q2021063) (← links)
• Deep learning of thermodynamics-aware reduced-order models from data (Q2021918) (← links)
• Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation (Q2059122) (← links)
• Non-intrusive data-driven model reduction for differential-algebraic equations derived from lifting transformations (Q2072439) (← links)
• Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs (Q2103427) (← links)
• Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems (Q2115511) (← links)
• Deep learning nonlinear multiscale dynamic problems using Koopman operator (Q2133546) (← links)
• The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations (Q2138717) (← 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)
• A pruning algorithm preserving modeling capabilities for polycrystalline data (Q2667331) (← links)
• Operator inference for non-intrusive model reduction with quadratic manifolds (Q2679511) (← links)
• Leveraging reduced-order models for state estimation using deep learning (Q5113091) (← 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)
• A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model (Q5864692) (← links)
• Transformers for modeling physical systems (Q6055222) (← links)
• CD-ROM: complemented deep -- reduced order model (Q6094649) (← links)
• Deep subspace encoders for nonlinear system identification (Q6136161) (← links)
• Level Set Learning with Pseudoreversible Neural Networks for Nonlinear Dimension Reduction in Function Approximation (Q6155903) (← links)
• Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds (Q6194167) (← links)
• Network flow problem heuristic reduction using machine learning (Q6547704) (← links)
• Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
• Non-intrusive parametric hyper-reduction for nonlinear structural finite element formulations (Q6669042) (← links)