Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples
- URL: http://arxiv.org/abs/2302.04945v1
- Date: Thu, 9 Feb 2023 21:28:15 GMT
- Title: Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples
- Authors: Danial Khatamsaz, Vahid Attari, Raymundo Arroyave, and Douglas L.
Allaire
- Abstract summary: Uncertainty propagation is a technique to determine model output uncertainties based on the uncertainty in its input variables.
In this work, we investigate the hypothesis that while all samples are useful on average, some samples must be more useful than others.
We introduce a methodology to adaptively reorder MC samples and show how it results in reduction of computational expense of UP processes.
- Score: 0.7087237546722617
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Uncertainty analysis in the outcomes of model predictions is a key element in
decision-based material design to establish confidence in the models and
evaluate the fidelity of models. Uncertainty Propagation (UP) is a technique to
determine model output uncertainties based on the uncertainty in its input
variables. The most common and simplest approach to propagate the uncertainty
from a model inputs to its outputs is by feeding a large number of samples to
the model, known as Monte Carlo (MC) simulation which requires exhaustive
sampling from the input variable distributions. However, MC simulations are
impractical when models are computationally expensive. In this work, we
investigate the hypothesis that while all samples are useful on average, some
samples must be more useful than others. Thus, reordering MC samples and
propagating more useful samples can lead to enhanced convergence in statistics
of interest earlier and thus, reducing the computational burden of UP process.
Here, we introduce a methodology to adaptively reorder MC samples and show how
it results in reduction of computational expense of UP processes.
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