Uncertainty-Aware Model-Constrained Neural Digital Twins for Real-Time Forecast, Calibration, and Control of Aeroelastic Airfoils

Abstract

Current structural health monitoring (SHM) in aeroelastic systems struggles with recovering unobservable states from limited sensor data, lacks quantified prediction confidence, and exhibits brittleness under off-nominal conditions. To address these operational gaps, this paper presents a physics aware neural digital twin (DT) designed for real-time, adaptive monitoring of aerospace structures. The proposed DT framework combines two neural networks for stabilizing control and forward prediction while using Bayesian filtering for nonlinear state estimation and uncertainty quantification. We train the controller using Proximal Policy Optimization (PPO) [1], an actor-critic method, and the forward model using the mcTangent loss [2]. We use Bayesian particle filter ing to infer the state and provide uncertainty quantification for assurance. The DT then provides the control input to the physical twin using the mean state estimate. Using only pitch angle measurements from a nonlinear airfoil model, the DT recovers full system state, stabilizes the structure rapidly, and provides uncertainty quantification (UQ) with all predictions. Finally, we compare the results to a more traditional approach of ensemble Kalman filter (EnKF) and model predictive control (MPC) and the base ODE model rather than the surrogate, which preforms worse due to worse early estimation of the hidden state. We also discuss the architecture’s transferability to broader digital twin applications across aerospace platforms.