101,308 research outputs found
Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA
It has become commonplace to use complex computer models to predict outcomes
in regions where data does not exist. Typically these models need to be
calibrated and validated using some experimental data, which often consists of
multiple correlated outcomes. In addition, some of the model parameters may be
categorical in nature, such as a pointer variable to alternate models (or
submodels) for some of the physics of the system. Here we present a general
approach for calibration in such situations where an emulator of the
computationally demanding models and a discrepancy term from the model to
reality are represented within a Bayesian Smoothing Spline (BSS) ANOVA
framework. The BSS-ANOVA framework has several advantages over the traditional
Gaussian Process, including ease of handling categorical inputs and correlated
outputs, and improved computational efficiency. Finally this framework is then
applied to the problem that motivated its design; a calibration of a
computational fluid dynamics model of a bubbling fluidized which is used as an
absorber in a CO2 capture system
Computer model calibration with large non-stationary spatial outputs: application to the calibration of a climate model
Bayesian calibration of computer models tunes unknown input parameters by
comparing outputs with observations. For model outputs that are distributed
over space, this becomes computationally expensive because of the output size.
To overcome this challenge, we employ a basis representation of the model
outputs and observations: we match these decompositions to carry out the
calibration efficiently. In the second step, we incorporate the non-stationary
behaviour, in terms of spatial variations of both variance and correlations, in
the calibration. We insert two integrated nested Laplace
approximation-stochastic partial differential equation parameters into the
calibration. A synthetic example and a climate model illustration highlight the
benefits of our approach
Calibrating an ice sheet model using high-dimensional binary spatial data
Rapid retreat of ice in the Amundsen Sea sector of West Antarctica may cause
drastic sea level rise, posing significant risks to populations in low-lying
coastal regions. Calibration of computer models representing the behavior of
the West Antarctic Ice Sheet is key for informative projections of future sea
level rise. However, both the relevant observations and the model output are
high-dimensional binary spatial data; existing computer model calibration
methods are unable to handle such data. Here we present a novel calibration
method for computer models whose output is in the form of binary spatial data.
To mitigate the computational and inferential challenges posed by our approach,
we apply a generalized principal component based dimension reduction method. To
demonstrate the utility of our method, we calibrate the PSU3D-ICE model by
comparing the output from a 499-member perturbed-parameter ensemble with
observations from the Amundsen Sea sector of the ice sheet. Our methods help
rigorously characterize the parameter uncertainty even in the presence of
systematic data-model discrepancies and dependence in the errors. Our method
also helps inform environmental risk analyses by contributing to improved
projections of sea level rise from the ice sheets
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