An open source crash course on parameter estimation of computational models using a Bayesian optimization approach

Abstract

Parameter estimation is a crucial aspect of computational modeling projects, especially the ones that deal with ordinary differential equations (ODE) or partial differential equation (PDE) models. Well-known examples in this regard are models derived from a basic balance or conservation law, such as mass balance or heat transfer problems. For real-world applications, these equations contain some coefficients that cannot be obtained directly from published scientific materials or experimental studies (Dehghan, 2001). One of the best solutions to this challenge is constructing an inverse problem.