Pages that link to "Item:Q3508105"

The following pages link to Multi-fidelity optimization via surrogate modelling (Q3508105):

Displaying 50 items.

• Variable-fidelity modeling of structural analysis of assemblies (Q280989) (← links)
• Nested maximin Latin hypercube designs (Q381446) (← links)
• Constrained multifidelity optimization using model calibration (Q381821) (← links)
• Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design (Q381914) (← links)
• Parameter tuning for a multi-fidelity dynamical model of the magnetosphere (Q386717) (← links)
• Mixture surrogate models based on Dempster-Shafer theory for global optimization problems (Q652658) (← links)
• Continuous multi-task Bayesian optimisation with correlation (Q724020) (← links)
• Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction (Q725460) (← links)
• Gaussian functional regression for output prediction: model assimilation and experimental design (Q729481) (← links)
• A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling (Q822137) (← links)
• Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection (Q905751) (← links)
• Optimization of aircraft structural components by using nature-inspired algorithms and multi-fidelity approximations (Q1037371) (← links)
• Gradient-based kriging approximate model and its application research to optimization design (Q1045380) (← links)
• A knowledge-based approach to response surface modelling in multifidelity optimization (Q1422890) (← links)
• Uncertainty quantification for a sailing yacht hull, using multi-fidelity Kriging (Q1646061) (← links)
• Large scale variable fidelity surrogate modeling (Q1680849) (← links)
• Multi-fidelity Gaussian process regression for prediction of random fields (Q1685592) (← links)
• Multi-fidelity meta-modeling for reservoir engineering - application to history matching (Q1693744) (← links)
• Reliable error estimation for Sobol' indices (Q1704013) (← links)
• Theoretical investigations of the new cokriging method for variable-fidelity surrogate modeling. Well-posedness and maximum likelihood training. (Q1756916) (← links)
• A critical appraisal of design of experiments for uncertainty quantification (Q1787391) (← links)
• Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics (Q1986728) (← links)
• Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling (Q1996002) (← links)
• Fractional calculus: quo vadimus? (where are we going?) (Q2017477) (← links)
• Distributed stochastic gradient tracking methods (Q2020611) (← links)
• Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization (Q2020786) (← links)
• Expected improvement for expensive optimization: a review (Q2022176) (← links)
• Surrogate optimization of deep neural networks for groundwater predictions (Q2046338) (← links)
• Multi-fidelity meta modeling using composite neural network with online adaptive basis technique (Q2060166) (← links)
• A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes (Q2075654) (← links)
• Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model (Q2084108) (← links)
• An intelligent multi-fidelity surrogate-assisted multi-objective reservoir production optimization method based on transfer stacking (Q2085102) (← links)
• Bi-fidelity reduced polynomial chaos expansion for uncertainty quantification (Q2115584) (← links)
• Multi-fidelity Bayesian neural networks: algorithms and applications (Q2124403) (← links)
• Augmented Gaussian random field: theory and computation (Q2129158) (← links)
• Neural network training using \(\ell_1\)-regularization and bi-fidelity data (Q2138992) (← links)
• Multi-fidelity design optimisation strategy under uncertainty with limited computational budget (Q2139153) (← links)
• A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems (Q2180467) (← links)
• Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering (Q2184447) (← links)
• Two-grid based adaptive proper orthogonal decomposition method for time dependent partial differential equations (Q2199697) (← links)
• Bi-fidelity stochastic gradient descent for structural optimization under uncertainty (Q2221705) (← links)
• Physics-informed cokriging: a Gaussian-process-regression-based multifidelity method for data-model convergence (Q2222351) (← links)
• A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems (Q2222703) (← links)
• Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders (Q2234140) (← links)
• Optimum parameters for each subject in bone remodeling models: a new methodology using surrogate and clinical data (Q2236328) (← links)
• Multi-fidelity analysis and uncertainty quantification of beam vibration using co-kriging interpolation method (Q2242133) (← links)
• Minimization of accident severity index in concrete barrier designs using an ensemble of radial basis function metamodel-based optimization (Q2245704) (← links)
• Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes (Q2246340) (← links)
• A fast multi-fidelity method with uncertainty quantification for complex data correlations: application to vortex-induced vibrations of marine risers (Q2246346) (← links)
• Engineering design applications of surrogate-assisted optimization techniques (Q2254199) (← links)