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 mor...
Saved in:
Main Authors: | Nick Pepper, Audrey Gaymann, Sanjiv Sharma, Francesco Montomoli |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/3db603d25349419baf2ed4038d973d20 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Approximate Bayesian computation.
by: Mikael Sunnåker, et al.
Published: (2013) -
Approximate analog computing with metatronic circuits
by: Mario Miscuglio, et al.
Published: (2021) -
Neural sensitization improves encoding fidelity in the primate retina
by: Todd R. Appleby, et al.
Published: (2019) -
Approximate Noether Symmetries of Perturbed Lagrangians and Approximate Conservation Laws
by: Matteo Gorgone, et al.
Published: (2021) - Computers & fluids