Deep Gaussian Processes for Probabilistic Prediction of UAV Propeller Aerodynamics

Abstract

This work evaluates Deep Gaussian Processes (DGPs) as probabilistic surrogate models for predicting thrust and torque of small Unmanned Aerial Vehicle (UAV) propellers. Unlike deterministic neural-network models, DGPs provide predictive uncertainty estimates in addition to mean predictions. Compared with standard Gaussian Processes, their hierarchical composition of multiple GP layers gives greater flexibility for representing complex nonlinear relationships between propeller geometry, operating conditions, and aerodynamic response. The models are trained and evaluated using experimental wind-tunnel data from 19 propellers with diameters between 7 and 20 inches. DGPs are compared with Multi-Layer Perceptrons and sparse Gaussian Processes using diameter-based train-test splits, where complete propeller diameters are withheld from training to assess generalization to unseen geometries. The results show that DGPs achieve competitive or improved predictive accuracy while providing more reliable uncertainty-aware predictions than the sparse Gaussian Process baseline. These findings indicate that DGPs are promising surrogate models for early-stage UAV propeller design, particularly when experimental data are limited and predictive uncertainty is important.