Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements

Hanieh Mousavi, Jeff D. Eldredge
1/6/2025
cs.LGphysics.flu-dyn

No official code found. Best implementation: haniehmsv/LowOrderFlowUQ (4 stars, MIT, last commit 2025-01-14).

Abstract

This paper presents a novel machine-learning framework for reconstructing low-order gust-encounter flow field and lift coefficients from sparse, noisy surface pressure measurements. Our study thoroughly investigates the time-varying response of sensors to gust-airfoil interactions, uncovering valuable insights into optimal sensor placement. To address uncertainties in deep learning predictions, we implement probabilistic regression strategies to model both epistemic and aleatoric uncertainties. Epistemic uncertainty, reflecting the model's confidence in its predictions, is modeled using Monte Carlo dropout, as an approximation to the variational inference in the Bayesian framework, treating the neural network as a stochastic entity. On the other hand, aleatoric uncertainty, arising from noisy input measurements, is captured via learned statistical parameters, which propagates measurement noise through the network into the final predictions. Our results showcase the efficacy of this dual uncertainty quantification strategy in accurately predicting aerodynamic behavior under extreme conditions while maintaining computational efficiency, underscoring its potential to improve online sensor-based flow estimation in real-world applications.

View on arXivView PDF

Code Implementations(1)

haniehmsv/LowOrderFlowUQCommunity100%
MIT

A machine-learning tool to predict flow states using sparse noisy pressure measurements. This code is capable of quantifying aleatoric (data) and epistemic (model) uncertainty.

40PythonJan 8, 20251 years agoMIT

Similar Papers

Uncertainty-Aware Flow Field Reconstruction Using SVGP Kolmogorov-Arnold Networks

85% match2025

Reconstructing unsteady flows from sparse, noisy measurements with a physics-constrained convolutional neural network

83% match1 repo62024

UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression

83% match2026

Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows

82% match2023

Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems

82% match2023

Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data

81% match2023

Cite this paper

@article{mousavi2025loworder,
  title  = {Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements},
  author = {Hanieh Mousavi and Jeff D. Eldredge},
  year   = {2025},
  eprint = {2501.03406},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.03406v1}
}

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/2501.03406.svg)](https://codeofpaper.com/paper/2501.03406)

Discussion