Scientific and engineering problems often involve parametric partial
dif...
Bayesian Physics Informed Neural Networks (B-PINNs) have gained signific...
When investigating epidemic dynamics through differential models, the
pa...
Multiscale elliptic equations with scale separation are often approximat...
The construction of efficient methods for uncertainty quantification in
...
Uncertainty in data is certainly one of the main problems in epidemiolog...
In this paper, we consider the development of efficient numerical method...
Gaussian Process (GP) regression is a flexible non-parametric approach t...
In this paper, we present a new nonintrusive reduced basis method when a...
In this work, we propose a framework that combines the
approximation-the...