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Found 4 papers, 2 papers with code
Gaussian process learning of nonlinear dynamics
Through a Bayesian scheme, a probabilistic estimate of the model parameters is given by the posterior distribution, and thus a quantification is facilitated for uncertainties from noisy state data and the learning process.
Bayesian approach to Gaussian process regression with uncertain inputs
Considering two types of observables -- noise-corrupted outputs with fixed inputs and those with prior-distribution-defined uncertain inputs, a posterior distribution is estimated via a Bayesian framework to infer the uncertain data locations.
Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots
Parametric reduced-order modelling often serves as a surrogate method for hemodynamics simulations to improve the computational efficiency in many-query scenarios or to perform real-time simulations.
Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling
An uncertainty quantification of a model for In-Stent Restenosis with four uncertain parameters (endothelium regeneration time, the threshold strain for smooth muscle cells bond breaking, blood flow velocity and the percentage of fenestration in the internal elastic lamina) is presented.
