CaliCo: a R package for Bayesian calibration

TL;DR

CaliCo is an R package designed to facilitate Bayesian calibration of numerical models, addressing computational challenges and discrepancies between models and experimental data, with practical guidelines and a real-world example.

Contribution

The paper introduces CaliCo, an R package that streamlines Bayesian calibration for complex models, incorporating multiple statistical models and user guidance.

Findings

Handles time-consuming codes efficiently

Addresses model-data discrepancies effectively

Provides practical implementation and example

Abstract

In this article, we present a recently released R package for Bayesian calibration. Many industrial fields are facing unfeasible or costly field experiments. These experiments are replaced with numerical/computer experiments which are realized by running a numerical code. Bayesian calibration intends to estimate, through a posterior distribution, input parameters of the code in order to make the code outputs close to the available experimental data. The code can be time consuming while the Bayesian calibration implies a lot of code calls which makes studies too burdensome. A discrepancy might also appear between the numerical code and the physical system when facing incompatibility between experimental data and numerical code outputs. The package CaliCo deals with these issues through four statistical models which deal with a time consuming code or not and with discrepancy or not. A guideline for users is provided in order to illustrate the main functions and their arguments. Eventually, a toy example is detailed using CaliCo. This example (based on a real physical system) is in five dimensions and uses simulated data.