REVIEW 1 cited by
Emerging trends in machine learning for computational fluid dynamics
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics (CFD). In particular, we discuss synergies between ML and CFD that have already shown benefits, and we also assess areas that are under development and may produce important benefits in the coming years. We believe that it is also important to emphasize a balanced perspective of cautious optimism for these emerging approaches
Forward citations
Cited by 1 Pith paper
-
An Interpretable Convolutional Neural Network Framework for Fluid Dynamics
A three-weight CNN learns the forward-Euler stencil from laminar flow data, generalizes to unseen conditions, analytical solutions, and molecular dynamics trajectories while remaining grounded in finite-difference operators.
Discussion (0). Continue with ORCID to comment.