Data-efficient operator learning for solving high Mach number fluid flow problems

Noah Ford, Victor J. Leon, Honest Mrema, Jeffrey Gilbert, Alexander New
11/28/2023
cs.LGmath.NAphysics.flu-dyn

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

We consider the problem of using SciML to predict solutions of high Mach fluid flows over irregular geometries. In this setting, data is limited, and so it is desirable for models to perform well in the low-data setting. We show that Neural Basis Functions (NBF), which learns a basis of behavior modes from the data and then uses this basis to make predictions, is more effective than a basis-unaware baseline model. In addition, we identify continuing challenges in the space of predicting solutions for this type of problem.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Data-Efficient Inference of Neural Fluid Fields via SciML Foundation Model

83% match1 repo62024

Geometry-Informed Neural Operator for Large-Scale 3D PDEs

82% match2026

3D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraints

82% match1 repo72025

Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning

81% match2023

Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems

81% match2024

A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations

81% match2025

Cite this paper

@article{ford2023dataefficient,
  title  = {Data-efficient operator learning for solving high Mach number fluid flow problems},
  author = {Noah Ford and Victor J. Leon and Honest Mrema and Jeffrey Gilbert and Alexander New},
  year   = {2023},
  eprint = {2311.16860},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.16860v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2311.16860.svg)](https://codeofpaper.com/paper/2311.16860)

Discussion