Pages that link to "Item:Q346358"
From MaRDI portal
The following pages link to Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification (Q346358):
Displaying 14 items.
- Bayesian latent variable co-kriging model in remote sensing for quality flagged observations (Q6050918) (← links)
- VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
- Transformers for modeling physical systems (Q6055222) (← links)
- Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers (Q6070085) (← links)
- Multifidelity approaches for uncertainty quantification (Q6088628) (← links)
- Probabilistic physics-guided transfer learning for material property prediction in extrusion deposition additive manufacturing (Q6185217) (← links)
- A hybrid data-driven-physics-constrained Gaussian process regression framework with deep kernel for uncertainty quantification (Q6187707) (← links)
- Diffusion maps-based surrogate modeling: an alternative machine learning approach (Q6497676) (← links)
- Learning to solve Bayesian inverse problems: an amortized variational inference approach using Gaussian and flow guides (Q6560691) (← links)
- An efficient metamodeling approach for uncertainty quantification of complex systems with arbitrary parameter probability distributions (Q6565211) (← links)
- Uncertainty modeling and propagation for groundwater flow: a comparative study of surrogates (Q6587664) (← links)
- Neural operator induced Gaussian process framework for probabilistic solution of parametric partial differential equations (Q6609778) (← links)
- An additive approximate Gaussian process model for large spatio-temporal data (Q6626104) (← links)
- Weak neural variational inference for solving Bayesian inverse problems \textit{without} forward models: applications in elastography (Q6663307) (← links)
