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Found 4 papers, 2 papers with code
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes
A digital twin is a computer model that represents an individual, for example, a component, a patient or a process.
Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors
In the studies, it is shown that our modelling framework can recover the true parameters of the physical models in cases where 1) the reality is more complex than our modelling choice and 2) the data acquisition process is biased while also producing accurate predictions.
A knowledge based spatial model for utilizing point and nested areal observations: A case study of annual runoff predictions in the Voss area
In this study, annual runoff is estimated by using a Bayesian geostatistical model for interpolating hydrological data of different spatial support.
Applications
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field
Estimation of stationary dependence structure parameters using only a single realisation of the spatial process, typically leads to inaccurate estimates and poorly identified parameters.
Methodology Applications
