Pages that link to "Item:Q385889"

The following pages link to Multi-output local Gaussian process regression: applications to uncertainty quantification (Q385889):

Displaying 48 items.

• A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media (Q340932) (← links)
• Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification (Q346358) (← links)
• Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation (Q726924) (← links)
• Uncertainty propagation using infinite mixture of gaussian processes and variational Bayesian inference (Q729001) (← links)
• Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems (Q729447) (← links)
• Sparse polynomial chaos expansions using variational relevance vector machines (Q781971) (← links)
• Multi-fidelity Gaussian process regression for prediction of random fields (Q1685592) (← links)
• Reduced-space Gaussian process regression for data-driven probabilistic forecast of chaotic dynamical systems (Q1691147) (← links)
• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification (Q1721865) (← links)
• A surrogate assisted adaptive framework for robust topology optimization (Q1986862) (← links)
• Systems of Gaussian process models for directed chains of solvers (Q1988026) (← links)
• Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273) (← links)
• Bayesian model calibration and optimization of surfactant-polymer flooding (Q2009830) (← links)
• An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty (Q2027200) (← links)
• Surrogate assisted active subspace and active subspace assisted surrogate -- a new paradigm for high dimensional structural reliability analysis (Q2072474) (← links)
• Optimal design for kernel interpolation: applications to uncertainty quantification (Q2124880) (← links)
• Transfer learning based multi-fidelity physics informed deep neural network (Q2127006) (← links)
• Clustered active-subspace based local Gaussian process emulator for high-dimensional and complex computer models (Q2134712) (← links)
• A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems (Q2138808) (← links)
• Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (Q2180429) (← links)
• Applying kriging proxies for Markov chain Monte Carlo in reservoir simulation (Q2192848) (← links)
• Local uncertainty sampling for large-scale multiclass logistic regression (Q2196246) (← links)
• Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← links)
• The role of surrogate models in the development of digital twins of dynamic systems (Q2241783) (← links)
• Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes (Q2246340) (← links)
• Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions (Q2374734) (← links)
• Gaussian process surrogates for failure detection: a Bayesian experimental design approach (Q2375069) (← links)
• Infinite-dimensional Lie algebras, representations, Hermitian duality and the operators of stochastic calculus (Q2422529) (← links)
• A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
• Projection pursuit adaptation on polynomial chaos expansions (Q2683425) (← links)
• Adaptive Simulation Selection for the Discovery of the Ground State Line of Binary Alloys with a Limited Computational Budget (Q4604867) (← links)
• Uncertainty Quantification with α-Stable-Process Models (Q4639562) (← links)
• High-Dimensional Nonlinear Multi-Fidelity Model with Gradient-Free Active Subspace Method (Q5162366) (← links)
• Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (Q5228359) (← links)
• BIAS MINIMIZATION IN GAUSSIAN PROCESS SURROGATE MODELING FOR UNCERTAINTY QUANTIFICATION (Q5412289) (← links)
• Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model (Q5741199) (← links)
• Deep capsule encoder–decoder network for surrogate modeling and uncertainty quantification (Q6082494) (← links)
• Multifidelity approaches for uncertainty quantification (Q6088628) (← links)
• Physics-informed information field theory for modeling physical systems with uncertainty quantification (Q6147081) (← links)
• A hybrid data-driven-physics-constrained Gaussian process regression framework with deep kernel for uncertainty quantification (Q6187707) (← links)
• A multi-output model based on extreme learning machine with application in the multi-objective optimization of a dental implant (Q6191945) (← links)
• Bi-fidelity variational auto-encoder for uncertainty quantification (Q6202982) (← links)
• Learning to solve Bayesian inverse problems: an amortized variational inference approach using Gaussian and flow guides (Q6560691) (← links)
• Computationally efficient nonstationary nearest-neighbor Gaussian process models using data-driven techniques (Q6626105) (← links)
• Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification (Q6639348) (← links)
• Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
• Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters (Q6663322) (← links)