Quantification of model uncertainty: Calibration, model discrepancy, and identifiability
By Paul D. Arendt, Daniel W. Apley, Wei Chen. 2012
339 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/85c12fd992a8028b6a69410ef49490624e27aba4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Statistical and Nonlinear Physics
In Statistics and Probability
Citations in the corpus
- A review of uncertainty analysis in building energy assessment (2018, methodology): “Moreover, the development of modern uncertainty quantification techniques provides more advanced methods and tools to facilitate the research on uncertainty analysis for a better understanding of the nature of building energy and associated energy models [7, 8].”
- A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “Most of these digital state parameters are calibrated offline, and only a small subset of the structural parameters is dynamically updated online with sensor data, likely to ensure good model identifiability (Arendt et al. 2012).”