Pages that link to "Item:Q2050400"

The following pages link to Model reduction and neural networks for parametric PDEs (Q2050400):

Displaying 50 items.

• Automated design parameter selection for neural networks solving coupled partial differential equations with discontinuities (Q388540) (← links)
• Deep learning observables in computational fluid dynamics (Q777521) (← links)
• Variational training of neural network approximations of solution maps for physical models (Q778301) (← links)
• Data driven approximation of parametrized PDEs by reduced basis and neural networks (Q782002) (← links)
• Non-intrusive reduced order modeling of nonlinear problems using neural networks (Q1656610) (← links)
• Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
• Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099) (← links)
• Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
• Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs (Q2103427) (← links)
• Solving parametric partial differential equations with deep rectified quadratic unit neural networks (Q2103467) (← links)
• Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← links)
• Analysis of heterogeneous structures of non-separated scales using curved bridge nodes (Q2138655) (← links)
• A comparison of neural network architectures for data-driven reduced-order modeling (Q2138791) (← links)
• The deep parametric PDE method and applications to option pricing (Q2161843) (← links)
• Reduced-order deep learning for flow dynamics. The interplay between deep learning and model reduction (Q2222675) (← links)
• An introduction to finite element methods for inverse coefficient problems in elliptic PDEs (Q2232366) (← links)
• Projection-based and neural-net reduced order model for nonlinear Navier-Stokes equations (Q2245826) (← links)
• A finite element based deep learning solver for parametric PDEs (Q2670366) (← links)
• Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
• Neural control of discrete weak formulations: Galerkin, least squares \& minimal-residual methods with quasi-optimal weights (Q2679332) (← links)
• Learning high-dimensional parametric maps via reduced basis adaptive residual networks (Q2679335) (← links)
• Foundations of Bayesian inference for complex statistical models. Abstracts from the workshop held May 2--8, 2021 (hybrid meeting) (Q2693003) (← links)
• Computation and learning in high dimensions. Abstracts from the workshop held August 1--7, 2021 (hybrid meeting) (Q2693017) (← links)
• The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
• (Q5053337) (← links)
• Two-Layer Neural Networks with Values in a Banach Space (Q5055293) (← links)
• Discretization of parameter identification in PDEs using neural networks (Q5058109) (← links)
• A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
• Nonlinear Reduced DNN Models for State Estimation (Q5095220) (← links)
• slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks (Q5095499) (← links)
• AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems (Q5099843) (← 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)
• DRIPS: a framework for dimension reduction and interpolation in parameter space (Q6048419) (← links)
• Conditional variational autoencoder with Gaussian process regression recognition for parametric models (Q6056206) (← links)
• A framework for machine learning of model error in dynamical systems (Q6076655) (← links)
• Mesh-informed neural networks for operator learning in finite element spaces (Q6077303) (← links)
• Fully probabilistic deep models for forward and inverse problems in parametric PDEs (Q6095115) (← links)
• Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations (Q6108133) (← links)
• Learning Markovian Homogenized Models in Viscoelasticity (Q6109142) (← links)
• Convergence Rates for Learning Linear Operators from Noisy Data (Q6109175) (← links)
• Neural Galerkin schemes with active learning for high-dimensional evolution equations (Q6117685) (← links)
• Deep convolutional Ritz method: parametric PDE surrogates without labeled data (Q6132294) (← links)
• A Data-Assisted Two-Stage Method for the Inverse Random Source Problem (Q6144051) (← links)
• Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems (Q6147083) (← links)
• Quantum Mechanics for Closure of Dynamical Systems (Q6150480) (← links)
• Connections between Operator-Splitting Methods and Deep Neural Networks with Applications in Image Segmentation (Q6151361) (← links)
• Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems (Q6153910) (← links)
• Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps (Q6153912) (← links)
• Level Set Learning with Pseudoreversible Neural Networks for Nonlinear Dimension Reduction in Function Approximation (Q6155903) (← links)
• Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (Q6159007) (← links)