Pages that link to "Item:Q1656610"

The following pages link to Non-intrusive reduced order modeling of nonlinear problems using neural networks (Q1656610):

Displaying 50 items.

• A comparison of neural network architectures for data-driven reduced-order modeling (Q2138791) (← links)
• A novel fractional-order neural network for model reduction of large-scale systems with fractional-order nonlinear structure (Q2156522) (← 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)
• A deep learning based reduced order modeling for stochastic underground flow problems (Q2162031) (← 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)
• Neural-network learning of SPOD latent dynamics (Q2168295) (← links)
• Data-driven reduced order model with temporal convolutional neural network (Q2175300) (← links)
• A nonlinear reduced order model with parametrized shape defects (Q2175315) (← 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)
• An artificial neural network framework for reduced order modeling of transient flows (Q2206568) (← links)
• Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem (Q2214654) (← links)
• Data driven governing equations approximation using deep neural networks (Q2222362) (← 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)
• A nonintrusive reduced order modelling approach using proper orthogonal decomposition and locally adaptive sparse grids (Q2222606) (← links)
• Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← 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)
• An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings (Q2243469) (← links)
• Projection-based and neural-net reduced order model for nonlinear Navier-Stokes equations (Q2245826) (← links)
• Data-driven model order reduction for problems with parameter-dependent jump-discontinuities (Q2246398) (← 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)
• Deep learning of biological models from data: applications to ODE models (Q2659803) (← links)
• Robust topology optimization with low rank approximation using artificial neural networks (Q2667321) (← links)
• A comparison of reduced-order modeling approaches using artificial neural networks for PDEs with bifurcating solutions (Q2672190) (← links)
• Finite element approximation of wave problems with correcting terms based on training artificial neural networks with fine solutions (Q2674053) (← links)
• Machine learning based refinement strategies for polyhedral grids with applications to virtual element and polyhedral discontinuous Galerkin methods (Q2675603) (← links)
• Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
• SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
• Accelerating algebraic multigrid methods via artificial neural networks (Q2679755) (← links)
• Parametric dynamic mode decomposition for reduced order modeling (Q2683069) (← links)
• DeepBND: a machine learning approach to enhance multiscale solid mechanics (Q2687559) (← links)
• A fast direct solver for non-intrusive reduced order modeling of vibroacoustic problems (Q2691965) (← links)
• The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
• 4 Modal methods for reduced order modeling (Q3384274) (← links)
• Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference (Q5146677) (← links)
• Model Order Reduction Framework for Problems with Moving Discontinuities (Q5152795) (← links)
• Non-Intrusive Reduced Order Modeling of Convection Dominated Flows Using Artificial Neural Networks with Application to Rayleigh-Taylor Instability (Q5163917) (← links)
• PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology (Q5864684) (← links)
• Numerical Assessment of a Nonintrusive Surrogate Model Based on Recurrent Neural Networks and Proper Orthogonal Decomposition: Rayleigh–Bénard Convection (Q5880415) (← links)
• Reduced basis methods for time-dependent problems (Q5887836) (← links)
• A data-driven surrogate modeling approach for time-dependent incompressible Navier-Stokes equations with dynamic mode decomposition and manifold interpolation (Q6038828) (← links)
• DRIPS: a framework for dimension reduction and interpolation in parameter space (Q6048419) (← links)
• Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Q6048987) (← links)