Pages that link to "Item:Q2002273"

The following pages link to Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273):

Displaying 50 items.

• Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ (Q4615657) (← links)
• Deep learning of vortex-induced vibrations (Q4647380) (← links)
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (Q4958918) (← links)
• An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks (Q5079536) (← links)
• slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks (Q5095499) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems (Q5162376) (← links)
• Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection (Q5162626) (← links)
• Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems (Q5163887) (← links)
• (Q5855574) (← links)
• A Model-Constrained Tangent Slope Learning Approach for Dynamical Systems (Q5880418) (← links)
• VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
• Transformers for modeling physical systems (Q6055222) (← links)
• Deep ReLU neural network approximation in Bochner spaces and applications to parametric PDEs (Q6062166) (← links)
• Probabilistic partition of unity networks for high‐dimensional regression problems (Q6062830) (← links)
• Deep capsule encoder–decoder network for surrogate modeling and uncertainty quantification (Q6082494) (← links)
• An <i>hp</i>‐adaptive multi‐element stochastic collocation method for surrogate modeling with information re‐use (Q6082501) (← links)
• Solving the discretised neutron diffusion equations using neural networks (Q6082621) (← links)
• Deep learning for thermal plasma simulation: solving 1-D arc model as an example (Q6097959) (← links)
• Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
• Conditional Karhunen-Loève regression model with basis adaptation for high-dimensional problems: uncertainty quantification and inverse modeling (Q6118544) (← links)
• Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control (Q6119249) (← links)
• Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy (Q6131423) (← links)
• Deep convolutional Ritz method: parametric PDE surrogates without labeled data (Q6132294) (← links)
• Collocation approximation by deep neural ReLU networks for parametric and stochastic PDEs with lognormal inputs (Q6148127) (← links)
• NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators (Q6154538) (← links)
• <tt>TNet</tt>: A Model-Constrained Tikhonov Network Approach for Inverse Problems (Q6154957) (← links)
• Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations (Q6154967) (← links)
• Level Set Learning with Pseudoreversible Neural Networks for Nonlinear Dimension Reduction in Function Approximation (Q6155903) (← links)
• Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction (Q6158090) (← links)
• A deep neural network-based method for solving obstacle problems (Q6158276) (← links)
• ReLU neural network Galerkin BEM (Q6159305) (← links)
• Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313) (← links)
• Numerical weighted integration of functions having mixed smoothness (Q6168703) (← links)
• Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events (Q6189161) (← links)
• Dual order-reduced Gaussian process emulators (DORGP) for quantifying high-dimensional uncertain crack growth using limited and noisy data (Q6194158) (← links)
• Quantification on the generalization performance of deep neural network with Tychonoff separation axioms (Q6195433) (← links)
• Bi-fidelity variational auto-encoder for uncertainty quantification (Q6202982) (← links)
• Data-driven modeling of partially observed biological systems (Q6537200) (← links)
• Multi-Fidelity Uncertainty Propagation Approach for Multi-Dimensional Correlated Flow Field Responses (Q6549081) (← links)
• A framework for strategic discovery of credible neural network surrogate models under uncertainty (Q6557831) (← links)
• Learning to solve Bayesian inverse problems: an amortized variational inference approach using Gaussian and flow guides (Q6560691) (← links)
• Wave-packet behaviors of the defocusing nonlinear Schrödinger equation based on the modified physics-informed neural networks (Q6562238) (← links)
• TDOR-MPINNs: multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein-Gordon-Zakharov systems (Q6568897) (← links)
• Random field of homogeneous and multi-material structures by the smoothed finite element method and Karhunen-Loève expansion (Q6578060) (← links)
• Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
• Neural and spectral operator surrogates: unified construction and expression rate bounds (Q6601288) (← links)
• Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)
• Accurate data-driven surrogates of dynamical systems for forward propagation of uncertainty (Q6648584) (← links)
• Sparse-grid sampling recovery and numerical integration of functions having mixed smoothness (Q6650705) (← links)