Pages that link to "Item:Q2180429"
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The following pages link to Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (Q2180429):
Displaying 24 items.
- Low-rank separated representation surrogates of high-dimensional stochastic functions: application in Bayesian inference (Q348754) (← links)
- Gradient-enhanced surrogate modeling based on proper orthogonal decomposition (Q455872) (← links)
- Theoretical investigations of the new cokriging method for variable-fidelity surrogate modeling. Well-posedness and maximum likelihood training. (Q1756916) (← links)
- A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (Q1987969) (← links)
- Decomposition-assisted computational technique based on surrogate modeling for real-time simulations (Q2012753) (← links)
- Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold (Q2020284) (← links)
- Surrogate assisted active subspace and active subspace assisted surrogate -- a new paradigm for high dimensional structural reliability analysis (Q2072474) (← links)
- Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (Q2182781) (← links)
- A distributed active subspace method for scalable surrogate modeling of function valued outputs (Q2211746) (← links)
- PLS-based adaptation for efficient PCE representation in high dimensions (Q2220565) (← links)
- Incremental modeling of a new high-order polynomial surrogate model (Q2290754) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- QUANTIFICATION OF UNCERTAINTY FROM HIGH-DIMENSIONAL SCATTERED DATA VIA POLYNOMIAL APPROXIMATION (Q2932949) (← links)
- Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (Q4995117) (← links)
- Surrogate Models for Oscillatory Systems Using Sparse Polynomial Chaos Expansions and Stochastic Time Warping (Q5269875) (← links)
- BIAS MINIMIZATION IN GAUSSIAN PROCESS SURROGATE MODELING FOR UNCERTAINTY QUANTIFICATION (Q5412289) (← links)
- Surrogate modeling for high dimensional uncertainty propagation via deep kernel polynomial chaos expansion (Q6072820) (← links)
- Conditional Karhunen-Loève regression model with basis adaptation for high-dimensional problems: uncertainty quantification and inverse modeling (Q6118544) (← links)
- Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems (Q6118558) (← links)
- Enhanced kriging leave-one-out cross-validation in improving model estimation and optimization (Q6171247) (← links)
- Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate (Q6198152) (← links)
- Fusing nonlinear solvers with transformers for accelerating the solution of parametric transient problems (Q6566042) (← links)
- A novel global prediction framework for multi-response models in reliability engineering using adaptive sampling and active subspace methods (Q6663323) (← links)
- A review of recent advances in surrogate models for uncertainty quantification of high-dimensional engineering applications (Q6663327) (← links)
