Bayesian Optimization Methods for Nonlinear Model Calibration

Bayesian Optimization Methods for Nonlinear Model Calibration

This work develops and compares seven Gaussian process Bayesian optimization (GPBO) methods for calibrating nonlinear models. We demonstrate through ten (non)linear parameter estimation examples that new BO methods using GP emulators of (computationally expensive) models accurately recovered paramet...

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Bibliographic Details
Main Author: Montana N. Carlozo (22175927) (author)
Other Authors: Ke Wang (82395) (author), Alexander W. Dowling (5462699) (author)
Published: 2025
Subjects:
Genetics
Biotechnology
Plant Biology
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
uses gp models
nonlinear model calibration
including genetic algorithms
free search algorithms
discuss future opportunities
calibrating nonlinear models
based nonlinear least
stochastic expensive models
expensive stochastic models
recommend emulator gpbo
emulator gpbo finds
benchmarking instances compared
bayesian optimization methods
instances compared
computationally expensive
standard gpbo
work develops
true parameters
popular derivative
loss objective
expected value
considering noisy
consider applications
approximately 0
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