# A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

arXiv: https://arxiv.org/abs/2606.28871 · HTML: https://mlsanity.com/paper/2606.28871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A Conceptual Introduction to Hamiltonian Monte Carlo](https://mlsanity.com/paper/1701.02434/) (2017)
- [Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization](https://mlsanity.com/paper/2008.06731/) (2020)
- [A theoretical framework for calibration in computer models: parametrization, estimation and convergence properties](https://mlsanity.com/paper/1508.07155/) (2015)
- [qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling](https://mlsanity.com/paper/2310.15788/) (2023)
- [Efficient Calibration for Imperfect Computer Models](https://mlsanity.com/paper/1507.07280/) (2015)
- [Contour Location for Reliability in Airfoil Simulation Experiments using Deep Gaussian Processes](https://mlsanity.com/paper/2308.04420/) (2023)
- [Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control](https://mlsanity.com/paper/2105.09824/) (2021)
- [Multiobjective Aerodynamic Design Optimization of the NASA Common Research Model](https://mlsanity.com/paper/2507.10488/) (2025)
- [Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning](https://mlsanity.com/paper/2109.02411/) (2021)
- [REMAL: Residual Equilibrium Manifold Active Learning for Surrogate-Based Multidisciplinary Design Analysis](https://mlsanity.com/paper/2606.13245/) (2026)

## Abstract (arXiv)

Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and direct simulations are prohibitive. A pragmatic way to overcome this limitation is by calibrating low-fidelity computational predictions with experimental measurements. This, however, requires calibrating against sparse measurements contaminated with uncertainty in both the control inputs and the measured aerodynamic response. We develop a methodology to address this problem based on Gaussian process surrogates and the classical Kennedy-O'Hagan calibration. A surrogate model learned on abundant-but-cheap low-fidelity data is calibrated with a sparse set of measurement data. Crucialy, we develop a Bayesian latent Gaussian process based approach that marginalizes the calibrated surrogate model over the input uncertainty, while also matching the marginal mean and variance of the measured output uncertainty. Once calibrated, our surrogate model predicts the uncertainty in aerodynamic coefficients with very high accuracy, including at extrapolative input settings. We validate our calibrated surrogate model predictions against measurement data with true uncertainty intervals to demonstrate that the model places 94.2-95.8% of its predictive samples inside the released 95% truth intervals, with endpoint cumulative probabilities very close to the nominal 0.025 and 0.975 levels.