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 two-stage deep learning architecture for model reduction of parametric time-dependent problems (Q6048996) (← links)
• Multiscale model reduction for incompressible flows (Q6052276) (← links)
• Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications (Q6052371) (← links)
• Conditional variational autoencoder with Gaussian process regression recognition for parametric models (Q6056206) (← links)
• Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis (Q6070094) (← links)
• Deep learning methods for partial differential equations and related parameter identification problems (Q6070739) (← links)
• Non-intrusive model order reduction for parametric radiation transport simulations (Q6078480) (← links)
• Non‐intrusive reduced‐order modeling using convolutional autoencoders (Q6092270) (← 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)
• Data assimilation predictive GAN (DA-PredGAN) applied to a spatio-temporal compartmental model in epidemiology (Q6101833) (← links)
• Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters (Q6101879) (← links)
• An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations (Q6110109) (← links)
• Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data (Q6113944) (← links)
• Branched latent neural maps (Q6118560) (← links)
• Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems (Q6119246) (← links)
• Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots (Q6119273) (← links)
• A graph convolutional autoencoder approach to model order reduction for parametrized PDEs (Q6126547) (← links)
• Operator inference with roll outs for learning reduced models from scarce and low-quality data (Q6135185) (← links)
• Model order reduction for parameterized electromagnetic problems using matrix decomposition and deep neural networks (Q6137793) (← links)
• Agglomeration of polygonal grids using graph neural networks with applications to multigrid solvers (Q6144203) (← links)
• Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems (Q6147083) (← links)
• An incremental singular value decomposition approach for large-scale spatially parallel \& distributed but temporally serial data -- applied to technical flows (Q6151883) (← links)
• A physics-based reduced order model for urban air pollution prediction (Q6153871) (← links)
• Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps (Q6153912) (← links)
• An artificial neural network approach to bifurcating phenomena in computational fluid dynamics (Q6158472) (← links)
• Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation (Q6159004) (← links)
• Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems (Q6185165) (← links)
• Active-learning-driven surrogate modeling for efficient simulation of parametric nonlinear systems (Q6185211) (← links)
• Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations (Q6187659) (← links)
• A numerical comparison of simplified Galerkin and machine learning reduced order models for vaginal deformations (Q6189301) (← links)
• A machine learning approach to enhance the SUPG stabilization method for advection-dominated differential problems (Q6195573) (← links)
• Kernel methods are competitive for operator learning (Q6202132) (← links)
• Error assessment of an adaptive finite elements -- neural networks method for an elliptic parametric PDE (Q6202970) (← links)
• Basis operator network: a neural network-based model for learning nonlinear operators via neural basis (Q6488825) (← links)
• An adaptive sampling algorithm for reduced-order models using isomap (Q6499911) (← links)
• Data-driven modeling of partially observed biological systems (Q6537200) (← links)
• Learning quantities of interest from parametric PDEs: an efficient neural-weighted minimal residual approach (Q6543646) (← links)
• An optimisation-based domain-decomposition reduced order model for parameter-dependent non-stationary fluid dynamics problems (Q6549892) (← links)
• Multi-scale time-stepping of partial differential equations with transformers (Q6550140) (← links)
• An efficient and robust method for parameterized nonintrusive reduced-order modeling (Q6553433) (← links)
• Inferring bifurcation diagrams with transformers (Q6555010) (← links)
• Reduction of the shallow water system by an error aware POD-neural network method: application to floodplain dynamics (Q6566077) (← links)
• Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation (Q6572185) (← links)
• Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models (Q6581233) (← links)
• Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
• Data-driven identification of stable sparse differential operators using constrained regression (Q6588307) (← links)
• Plug-and-play adaptive surrogate modeling of parametric nonlinear dynamics in frequency domain (Q6592366) (← links)
• Non-intrusive reduced-order model for time-dependent stochastic partial differential equations utilizing dynamic mode decomposition and polynomial chaos expansion (Q6592584) (← links)