Development of a Data-driven Surrogate Model for Scale-bridging in Battery Modelling Application

In numerous mechanical engineering applications, the use of multiscale computational modeling and simulations is imperative. Nevertheless, the computational challenge persists in addressing complex multiscale systems due to the vast dimensionality of the solution space. The field of machine learning (ML) has experienced ongoing development as a feasible option that might potentially expedite, substitute, or complement traditional numerical techniques.