On the development of a practical Bayesian optimization algorithm for expensive experiments and simulations with changing environmental conditions
Experiments in engineering are typically conducted in controlled environments where parameters can be set to any desired value. This assumes that the same applies in a real-world setting, which is often incorrect as many experiments are influenced by uncontrollable environmental conditions such as t...
Saved in:
| Main Authors: | Mike Diessner, Kevin J. Wilson, Richard D. Whalley |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2024-01-01
|
| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673624000406/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Constrained Bayesian Optimization: A Review
by: Sasan Amini, et al.
Published: (2025-01-01) -
Efficient Tuning of an Isotope Separation Online System Through Safe Bayesian Optimization with Simulation-Informed Gaussian Process for the Constraints
by: Santiago Ramos Garces, et al.
Published: (2024-11-01) -
Advanced Monte Carlo for Acquisition Sampling in Bayesian Optimization
by: Javier Garcia-Barcos, et al.
Published: (2025-01-01) -
A Bayesian Optimization Approach for Tuning a Grouping Genetic Algorithm for Solving Practically Oriented Pickup and Delivery Problems
by: Cornelius Rüther, et al.
Published: (2024-02-01) -
Anatomical Plausibility in Deformable Image Registration Using Bayesian Optimization for Brain MRI Analysis
by: Mauricio Castaño-Aguirre, et al.
Published: (2024-11-01)