Pages that link to "Item:Q5235633"

The following pages link to Construction of reduced-order models for fluid flows using deep feedforward neural networks (Q5235633):

Displaying 30 items.

• A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems (Q2020779) (← links)
• Data-driven nonintrusive reduced order modeling for dynamical systems with moving boundaries using Gaussian process regression (Q2020804) (← links)
• Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms (Q2084088) (← links)
• Calibration of projection-based reduced-order models for unsteady compressible flows (Q2120780) (← links)
• Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning (Q2127404) (← links)
• Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow (Q2131089) (← links)
• Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields (Q2132659) (← links)
• DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators (Q2133505) (← links)
• Unsteady flow prediction from sparse measurements by compressed sensing reduced order modeling (Q2138820) (← links)
• A deep learning based reduced order modeling for stochastic underground flow problems (Q2162031) (← links)
• Data-driven reduced order model with temporal convolutional neural network (Q2175300) (← links)
• Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (Q2190672) (← links)
• An artificial neural network framework for reduced order modeling of transient flows (Q2206568) (← links)
• Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning (Q2222332) (← links)
• Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition (Q2237770) (← links)
• Projection-based and neural-net reduced order model for nonlinear Navier-Stokes equations (Q2245826) (← links)
• Data-driven closure of projection-based reduced order models for unsteady compressible flows (Q2246325) (← links)
• Parametric dynamic mode decomposition for reduced order modeling (Q2683069) (← links)
• Stochastic modelling of a noise-driven global instability in a turbulent swirling jet (Q3389421) (← links)
• Neural networks for BEM analysis of steady viscous flows (Q4422586) (← links)
• Optimization-Based Modal Decomposition for Systems with Multiple Transports (Q4997436) (← 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)
• Deep learning‐based reduced order models for the real‐time simulation of the nonlinear dynamics of microstructures (Q6071430) (← links)
• Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains (Q6092912) (← links)
• Unsteady reduced order model with neural networks and flight-physics-based regularization for aerodynamic applications (Q6093461) (← links)
• Artificial neural network based correction for reduced order models in computational fluid mechanics (Q6096479) (← links)
• Learning proper orthogonal decomposition of complex dynamics using heavy-ball neural ODEs (Q6101554) (← links)
• \textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications (Q6116133) (← links)
• A highly accurate strategy for data-driven turbulence modeling (Q6125393) (← links)
• An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots (Q6574153) (← links)