Multi-fidelity aerodynamic data fusion by autoencoder transfer learning

TL;DR

This paper introduces a multi-fidelity deep learning approach that uses autoencoders and transfer learning to accurately predict aerodynamic data with minimal high-fidelity samples, while also providing reliable uncertainty estimates.

Contribution

It develops a novel autoencoder transfer learning framework combined with a Multi-Split Conformal Prediction strategy for uncertainty-aware aerodynamic data fusion under extreme data scarcity.

Findings

High-accuracy pressure predictions with minimal high-fidelity data

Robust uncertainty bands with over 95% coverage

Effective correction of low-fidelity deviations

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.