The following pages link to GPy (Q26204):
Displaying 38 items.
- Comparison of Gaussian process modeling software (Q65764) (← links)
- Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming (Q91882) (← links)
- Novel algorithm using active metamodel learning and importance sampling: application to multiple failure regions of low probability (Q725434) (← links)
- Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation (Q726924) (← links)
- Understanding hormonal crosstalk in \textit{Arabidopsis} root development via emulation and history matching (Q830633) (← links)
- Stochastic variational hierarchical mixture of sparse Gaussian processes for regression (Q1722733) (← links)
- A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (Q1735988) (← links)
- An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques (Q2024169) (← links)
- A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew Gaussian processes (Q2071487) (← links)
- Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (Q2072477) (← links)
- A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations (Q2083198) (← links)
- Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: extension to geometrical parameterizations (Q2096859) (← links)
- A robust approach to warped Gaussian process-constrained optimization (Q2097663) (← links)
- ROmodel: modeling robust optimization problems in pyomo (Q2101652) (← links)
- Physics-informed machine learning with conditional Karhunen-Loève expansions (Q2126979) (← links)
- Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems (Q2136471) (← links)
- A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics (Q2167597) (← links)
- Quantile-based robust optimization of a supersonic nozzle for organic rankine cycle turbines (Q2174738) (← links)
- Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression (Q2175100) (← links)
- A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems (Q2180467) (← links)
- Recursive estimation for sparse Gaussian process regression (Q2203074) (← links)
- Skew Gaussian processes for classification (Q2217445) (← links)
- Inflation as an information bottleneck: a strategy for identifying universality classes and making robust predictions (Q2315702) (← links)
- Antithetic and Monte Carlo kernel estimators for partial rankings (Q2329828) (← links)
- Data-driven surrogate modeling of multiphase flows using machine learning techniques (Q2664065) (← links)
- Machine learning constitutive models of elastomeric foams (Q2670325) (← links)
- Optimal experiment design for a bottom friction parameter estimation problem (Q2671734) (← links)
- Output-weighted and relative entropy loss functions for deep learning precursors of extreme events (Q2677801) (← links)
- pyGPs -- a Python library for Gaussian process regression and classification (Q2788375) (← links)
- Variational inference for latent variables and uncertain inputs in Gaussian processes (Q2810833) (← links)
- String and membrane Gaussian processes (Q2834447) (← links)
- (Q4558140) (← links)
- Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling (Q4647131) (← links)
- (Q4969236) (← links)
- Faster Kriging: Facing High-Dimensional Simulators (Q5130493) (← links)
- (Q5214266) (← links)
- Known Boundary Emulation of Complex Computer Models (Q5237178) (← links)
- GPflow: a Gaussian process library using TensorFlow (Q5361306) (← links)
