Pages that link to "Item:Q1721865"

The following pages link to Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification (Q1721865):

Displaying 50 items.

• A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (Q776737) (← links)
• Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks (Q1989082) (← links)
• Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273) (← links)
• Parametric generation of conditional geological realizations using generative neural networks (Q2009823) (← links)
• Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800) (← links)
• Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (Q2021999) (← links)
• An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty (Q2027200) (← links)
• Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
• Active learning Bayesian support vector regression model for global approximation (Q2054106) (← links)
• Bayesian distillation of deep learning models (Q2069701) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
• A Bayesian multiscale deep learning framework for flows in random media (Q2072635) (← links)
• A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
• Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
• An intelligent multi-fidelity surrogate-assisted multi-objective reservoir production optimization method based on transfer stacking (Q2085102) (← links)
• Stochastic modeling of inhomogeneities in the aortic wall and uncertainty quantification using a Bayesian encoder-decoder surrogate (Q2096832) (← links)
• Multifidelity data fusion in convolutional encoder/decoder networks (Q2099723) (← links)
• Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models (Q2112502) (← links)
• A deep domain decomposition method based on Fourier features (Q2112697) (← links)
• Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method (Q2113241) (← links)
• GINNs: graph-informed neural networks for multiscale physics (Q2120776) (← links)
• Solving inverse problems using conditional invertible neural networks (Q2120777) (← links)
• An efficient neural network method with plane wave activation functions for solving Helmholtz equation (Q2122592) (← links)
• Data-driven deep learning of partial differential equations in modal space (Q2123370) (← links)
• A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
• On generalized residual network for deep learning of unknown dynamical systems (Q2124404) (← links)
• Transfer learning based multi-fidelity physics informed deep neural network (Q2127006) (← links)
• Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake (Q2130972) (← links)
• Extended dynamic mode decomposition for inhomogeneous problems (Q2132641) (← links)
• Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← links)
• Mesh-Conv: convolution operator with mesh resolution independence for flow field modeling (Q2133572) (← links)
• Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems (Q2133766) (← links)
• Adaptive deep density approximation for Fokker-Planck equations (Q2135831) (← links)
• Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach (Q2136715) (← links)
• Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models (Q2138012) (← links)
• A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems (Q2138808) (← links)
• Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
• A heteroencoder architecture for prediction of failure locations in porous metals using variational inference (Q2160437) (← links)
• Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
• A data-driven surrogate to image-based flow simulations in porous media (Q2176870) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (Q2180429) (← links)
• A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations (Q2184449) (← links)
• Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (Q2214626) (← links)
• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275) (← links)
• ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287) (← 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)
• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (Q2222519) (← links)