Gaussian Process Modeling of a Functional Output with Information from Boundary and Initial Conditions and Analytical Approximations
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Publication:6622423
DOI10.1080/00401706.2017.1345702MaRDI QIDQ6622423
[[Person:6622422|Author name not available (Why is that?)]]
Publication date: 22 October 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
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- Title not available (Why is that?)
- Bayesian solution uncertainty quantification for differential equations
- Uncertainty assessment of large finite element systems.
- Bayesian emulation of complex multi-output and dynamic computer models
- The design and analysis of computer experiments.
- A new stochastic approach to transient heat conduction modeling with uncertainty.
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- Remarks around 50 lines of Matlab: short finite element implementation
- Emulation of Multivariate Simulators Using Thin-Plate Splines with Application to Atmospheric Dispersion
- Partial Differential Equations and the Finite Element Method
- Computer Model Calibration Using High-Dimensional Output
- Shepard's Method of "Metric Interpolation" to Bivariate and Multivariate Interpolation
- Regression Analysis for a Functional Response
- Gaussian Process Modeling with Boundary Information
- Predicting the output from a complex computer code when fast approximations are available
- Partial Differential Equations
Related Items (4)
Multi-fidelity Gaussian process modeling with boundary information ⋮ Gaussian Process Modeling Using the Principle of Superposition ⋮ A Gaussian Process Emulator Based Approach for Bayesian Calibration of a Functional Input ⋮ Mesh-Clustered Gaussian Process Emulator for Partial Differential Equation Boundary Value Problems
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