Pages that link to "Item:Q2214654"

The following pages link to Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem (Q2214654):

Displaying 32 items.

• 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)
• Nonintrusive Reduced Order Modelling of Convective Boussinesq Flows (Q5880413) (← links)
• Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← 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)
• Conditional variational autoencoder with Gaussian process regression recognition for parametric models (Q6056206) (← links)
• A non‐linear non‐intrusive reduced order model of fluid flow by auto‐encoder and self‐attention deep learning methods (Q6060947) (← links)
• An adaptive reduced order model for the angular discretization of the Boltzmann transport equation using independent basis sets over a partitioning of the space‐angle domain (Q6070103) (← links)
• Unsteady reduced order model with neural networks and flight-physics-based regularization for aerodynamic applications (Q6093461) (← links)
• Surrogate modeling of time-domain electromagnetic wave propagation via dynamic mode decomposition and radial basis function (Q6095088) (← links)
• Artificial neural network based correction for reduced order models in computational fluid mechanics (Q6096479) (← 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-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters (Q6101879) (← links)
• Front transport reduction for complex moving fronts (Q6111405) (← links)
• Neural Galerkin schemes with active learning for high-dimensional evolution equations (Q6117685) (← links)
• Model order reduction for parameterized electromagnetic problems using matrix decomposition and deep neural networks (Q6137793) (← links)
• Reduced-order model-based variational inference with normalizing flows for Bayesian elliptic inverse problems (Q6145183) (← links)
• An artificial neural network approach to bifurcating phenomena in computational fluid dynamics (Q6158472) (← links)
• Active-learning-driven surrogate modeling for efficient simulation of parametric nonlinear systems (Q6185211) (← links)
• Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure (Q6194176) (← links)
• A finite element reduced-order model based on adaptive mesh refinement and artificial neural networks (Q6497675) (← links)
• Investigation of combustion model via the local collocation technique based on moving Taylor polynomial (MTP) approximation/domain decomposition method with error analysis (Q6545724) (← links)
• An efficient and robust method for parameterized nonintrusive reduced-order modeling (Q6553433) (← links)
• Reduction of the shallow water system by an error aware POD-neural network method: application to floodplain dynamics (Q6566077) (← links)
• Non-intrusive reduced-order model for time-dependent stochastic partial differential equations utilizing dynamic mode decomposition and polynomial chaos expansion (Q6592584) (← links)
• Domain decomposition for physics-data combined neural network based parametric reduced order modelling (Q6639365) (← links)
• PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
• A data-driven reduced-order modeling approach for parameterized time-domain Maxwell's equations (Q6647127) (← links)
• Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning (Q6652579) (← links)
• Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems (Q6667327) (← links)