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Showing 1 to 15 of 15 for “"Bayesian calibration"”.
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EXCALIBRATE Bayesian calibration for data-intensive astrophysical experimentation
… by the unprecedented levels of sensitivity and calibration needed to confidently distinguish these millikelvin-level signatures from galactic foregrounds and instrument systematics. In this work we detail the development of a calibration methodology that expands upon the Dicke switching …
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Bayesian calibration of in-line inspection tool tolerance
Calibration of Magnetic Flux Leakage (MFL) In-line Inspection (ILI) tools is an important part of the overall pipeline integrity management process. Over-called or under-called corrosion features can have significant impacts on safety and resource management. This thesis examines methods for …
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Bayesian Calibration for Logit Model Microsimulations: Case for PECAS SD in San Diego
Bayesian inference is a versatile method for incorporating new information into a model while still respecting existing knowledge. One application of Bayesian inference is the calibration of models that are controlled by a large number of parameters, but where the data usable for calibration is …
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More Efficient Sampling for Bayesian Calibration of Computationally Expensive Hydrologic and Environmental Simulation Models
In recent years, interest in Bayesian calibration of hydrologic and environmental simulation (HE) models has been growing. The Bayesian approach allows for making statistical inference on unknown parameters and has proved to be an effective method to treat the estimation uncertainty arising from …
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A hierarchical Bayesian calibration framework for quantifying input uncertainties in thermal-hydraulics simulation models
… ``lack of input uncertainty information'' issue. Bayesian calibration, or inverse Uncertainty Quantification (UQ), is the process of updating uncertainty distributions on the model inputs in a way that is consistent with observed data. The process of Bayesian calibration for nuclear system codes …
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Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive Manufacturing
… using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the …
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Uncertainty quantification and calibration of physical models
… ecosystem model is computationally very heavy. Bayesian calibration method has been used as an efficient way to calibrate and quantify uncertainties of the computer models. In this work, I develop a new approach to emulate the TEM, and to estimate the parameters along with associated …
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Uncertainty quantification and calibration in nuclear safety codes using Gaussian process active learning
… be used in place of the computer code to perform Bayesian calibration techniques to determine uncertain parameter distribution. The performance of a GP emulator is largely dependent on the quality of the points in its training set; the best emulator exactly replicates the output of the computer …
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Assessment of Model Validation, Calibration, and Prediction Approaches in the Presence of Uncertainty
… are used for addressing model validation and calibration: 1) the area validation metric (AVM), 2) a modified area validation metric (MAVM) with confidence intervals, 3) the standard validation uncertainty from ASME VandV 20, and 4) Bayesian updating of a model discrepancy term. Details are …
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Contributions to predicting contaminant leaching from secondary materials used in roads
… and embankment hydrology and reactive transport, Bayesian statistics, and aqueous geochemistry of leaching.</p><p>Contributions on water movement and reactive transport in highways included probabilistic prediction of leaching in an embankment, and scenario analyses of leaching and transport in …
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Data-driven Methods in Mechanical Model Calibration and Prediction for Mesostructured Materials
… necessitates novel and effective approaches of calibration. A calibration framework based in Bayesian inference, which integrates data from simulations and physical experiments, has been applied to a study involving a mesostructured material fabricated by fused deposition modeling. Calibration …
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Bayesian Methods for Mineral Processing Operations
… can have a prohibitive computation time. Bayesian statistical methods intrinsically quantify uncertainty of model parameters and predictions given a set of data and a prior distribution and model parameter prior distributions. The uncertainty quantification possible with Bayesian methods …
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A fully Bayesian approach to uncertainty quantification of groundwater models
… measured, they are normally estimated by model calibration. In addition, groundwater models are often subject to input data errors, as some of the input forcings (such as recharge and well pumping rates) are unknown or estimated. Furthermore, model structural error is ubiquitous, due to …
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Application of the hector-brick reduced complexity earth system model for probabilistic climate projection
… Hector-BRICK reduced complexity climate model in Bayesian calibrations that assimilate information from global observational data sets to estimate model parameters. These calibrations produce sets of model parameters that are consistent with observational constraints and account for correlations …
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Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
… We formulate the inverse UQ process under the Bayesian framework using the ``model updating equation''. Markov Chain Monte Carlo (MCMC) sampling is applied to explore the posterior distributions and generate samples from which we can extract statistical information for the uncertain input …