Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models — Paper

Abstract

-cross Abstract: Neural network subgrid stress models often have a priori performance that is far better than the a posteriori performance, leading to neural network models that look very promising a priori completely failing in a posteriori Large Eddy Simulations (LES). This performance gap can be decreased by combining two different methods, training data augmentation and reducing input complexity to the neural network. Augmenting the training data with two different filters before training the neural networks has no performance degradation a priori as compared to a neural network trained with one filter. A posteriori, neural networks trained with two different filters are far more robust across two different LES codes with different numerical schemes. In addition, by ablating away the higher order terms input into the neural network, the a priori versus a posteriori performance changes become less apparent. When combined, neural networks that use both training data augmentation and a less complex set of inputs have a posteriori performance far more reflective of their a priori evaluation.

Authors 2

Andy Wu, Sanjiva K. Lele

Specification

Official page: arxiv.org/abs/2511.17475
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
arXiv id: 2511.17475
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Categories: physics.flu-dyn, cs.LG
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
DOI: 10.1017/jfm.2026.11995
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
PDF: arxiv.org/pdf/2511.17475
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Primary category: physics.flu-dyn
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

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10

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T110

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5 h ago

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Authors: Andy Wu, Sanjiva K. Lele

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Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2511.17475	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperAddressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models
New paper: Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	4 h ago	1

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