Multi-fidelity aerodynamic data fusion by autoencoder transfer learning

• URL: http://arxiv.org/abs/2512.13069v1
• Date: Mon, 15 Dec 2025 08:06:52 GMT
• Title: Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
• Abstract summary: This work presents a multi-fidelity deep learning framework to achieve uncertainty-aware aerodynamic data fusion.<n>It is tested on surface-pressure distributions for NACA airfoils (2D) and a transonic wing (3D) databases.<n>The framework produces robust, actionable uncertainty bands with pointwise coverage exceeding 95%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling. This limitation motivates the development of multi-fidelity strategies that leverage inexpensive low-fidelity information without compromising accuracy. Addressing this challenge, this work presents a multi-fidelity deep learning framework that combines autoencoder-based transfer learning with a newly developed Multi-Split Conformal Prediction (MSCP) strategy to achieve uncertainty-aware aerodynamic data fusion under extreme data scarcity. The methodology leverages abundant Low-Fidelity (LF) data to learn a compact latent physics representation, which acts as a frozen knowledge base for a decoder that is subsequently fine-tuned using scarce HF samples. Tested on surface-pressure distributions for NACA airfoils (2D) and a transonic wing (3D) databases, the model successfully corrects LF deviations and achieves high-accuracy pressure predictions using minimal HF training data. Furthermore, the MSCP framework produces robust, actionable uncertainty bands with pointwise coverage exceeding 95%. By combining extreme data efficiency with uncertainty quantification, this work offers a scalable and reliable solution for aerodynamic regression in data-scarce environments.

Related papers

• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• Uncertainty Quantification for Multi-fidelity Simulations [0.0]
  The work focuses on gathering high-fidelity and low-fidelity numerical simulations data using Nektar++ and XFOIL respectively.<n>The utilization of the higher distribution in calculating the Coefficient of lift and drag has demonstrated superior accuracy and precision.<n>To minimize the reliability on high-fidelity numerical simulations in Uncertainty Quantification, a multi-fidelity strategy has been adopted.
  arXiv  Detail & Related papers  (2025-03-11T13:11:18Z)
• LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery [5.373182035720355]
  Langevin-Assisted Active Physical Discovery (LAPD) is a framework that integrates replica-exchange gradient Langevin Monte Carlo.<n>LAPD achieves reliable, uncertainty-aware identification with noisy data.<n>We evaluate LAPD on diverse nonlinear systems such as the Lotka-Volterra, Lorenz, Burgers, and Convection-Diffusion equations.
  arXiv  Detail & Related papers  (2025-03-04T20:17:24Z)
• Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations [6.03891813540831]
  Laplace Neural Operators (LNOs) have emerged as a promising approach in scientific machine learning.<n>We propose multi-fidelity Laplace Neural Operators (MF-LNOs), which combine a low-fidelity base model with parallel linear/nonlinear HF correctors and dynamic inter-fidelity weighting.<n>This allows us to exploit correlations between LF and HF datasets and achieve accurate inference of quantities of interest.
  arXiv  Detail & Related papers  (2025-02-01T20:38:50Z)
• MIBP-Cert: Certified Training against Data Perturbations with Mixed-Integer Bilinear Programs [50.41998220099097]
  Data errors, corruptions, and poisoning attacks during training pose a major threat to the reliability of modern AI systems.<n>We introduce MIBP-Cert, a novel certification method based on mixed-integer bilinear programming (MIBP)<n>By computing the set of parameters reachable through perturbed or manipulated data, we can predict all possible outcomes and guarantee robustness.
  arXiv  Detail & Related papers  (2024-12-13T14:56:39Z)
• NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression [34.30562110131907]
  NeurLZ is a neural method designed to enhance lossy compression by integrating online learning, cross-field learning, and robust error regulation.<n>During the first five learning epochs, NeurLZ achieves an 89% bit rate reduction, with further optimization yielding up to around 94% reduction at equivalent distortion.
  arXiv  Detail & Related papers  (2024-09-09T16:48:09Z)
• Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows [43.148818844265236]
  Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before. High-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space. We extend a DeepONet using three different uncertainty mechanisms: mean-variance estimation, evidential uncertainty, and ensembling.
  arXiv  Detail & Related papers  (2023-10-31T18:07:29Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator [10.104417481736833]
  This work proposes a novel multi-fidelity learning method based on the Fourier Neural Operator. It uses abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm. Three typical fluid and temperature prediction problems are chosen to validate the accuracy of the proposed multi-fidelity model.
  arXiv  Detail & Related papers  (2023-04-14T07:46:03Z)
• HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse [49.1501082763252]
  This paper presents HFEDMS for incorporating practical FL into the emerging Industrial Metaverse. It reduces data heterogeneity through dynamic grouping and training mode conversion. Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics. Experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices.
  arXiv  Detail & Related papers  (2022-11-07T04:33:24Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Semantic Perturbations with Normalizing Flows for Improved Generalization [62.998818375912506]
  We show that perturbations in the latent space can be used to define fully unsupervised data augmentations. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective.
  arXiv  Detail & Related papers  (2021-08-18T03:20:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.