Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations
An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or random search over the model input space, are computational...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2023-12-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0151747 |