Local bi-fidelity field approximation with Knowledge Based Neural Networks for Computational Fluid Dynamics.

ABSTRACT: This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a more accurate, but expensive, computational model. An advantage of the method is that it can scale easily with the number of input and output features. This allows bi-fidelity modelling approaches to be applied to a wide variety of problems, for instance in the bi-fidelity modelling of fields. We demonstrate this aspect in this work through an application to Computational Fluid Dynamics, in which local corrections to a velocity field are performed by the KBaNN to account for mesh effects. KBaNNs were traine

SUBMITTER: Pepper N 

PROVIDER: S-EPMC8280202 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature