Distance-Distributed Design for Gaussian Process Surrogates
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Publication:6631860
DOI10.1080/00401706.2019.1677269MaRDI QIDQ6631860
D. Austin Cole, Robert B. Gramacy, Boya Zhang
Publication date: 1 November 2024
Published in: Technometrics (Search for Journal in Brave)
Cites Work
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- Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments
- Exploratory designs for computational experiments
- Minimax optimal designs via particle swarm optimization methods
- Design of computer experiments: space filling and beyond
- A new and flexible method for constructing designs for computer experiments
- Efficient global optimization of expensive black-box functions
- The design and analysis of computer experiments.
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Analysis methods for computer experiments: how to assess and what counts?
- Batch sequential designs for computer experiments
- Bayesian Calibration of Computer Models
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Combining Field Data and Computer Simulations for Calibration and Prediction
- A Limited Memory Algorithm for Bound Constrained Optimization
- Convergence rates of efficient global optimization algorithms
- Replication or Exploration? Sequential Design for Stochastic Simulation Experiments
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