# Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles
## 1 Introduction  
Accurately predicting aerodynamic performance is fundamental to the design and optimization of airfoils employed in aircraft, wind turbines, unmanned aerial vehicles (UAVs), and drones. Traditionally, this process is heavily dependent on high-fidelity computational fluid dynamics (CFD). While effective, CFD remains one of the most resource-intensive tools available in current aerodynamic design workflows. For example, [PERSON] et al. [1] evaluated finite-volume operations per second across hardware architectures (AMD, Intel, Arm) using OpenFOAM benchmarks, demonstrating the considerable computational costs involved in CFD workflows. Regarding ANSYS Fluent applications, a recent study by [PERSON] et al. [2] evaluated GPU-accelerated CFD performance, comparing compute speed, power consumption, and service-unit cost across CPU and GPU architectures. While less reliable than CFD, XFOIL simulations are proven to be much faster and serve as a practical alternative for establishing the initial aerodynamic performance of airfoils. As shown in [PERSON] et al. [3], XFOIL completed solutions in seconds to minutes, whereas CFD runs required significantly more time due to mesh generation and solver iterations. In response to this challenge, a new direction is created by leveraging machine learning (ML) as a data-driven alternative for aerodynamic prediction. When provided with enough high-quality data, ML models can deliver fast and reliable performance estimates that are useful in early-stage design or low-cost applications such as drones and small UAVs. Recent studies have explored various artificial intelligence (AI) approaches, specifically ML architectures, to predict aerodynamic characteristics. On a global review standpoint, different studies cover ML applications across aerospace engineering, including aerodynamic prediction and turbulence modeling [4], wind farms [5], UAV operations [6], and drone technology [7]. For instance, [PERSON] and [PERSON] [8] utilized AI to predict the lift coefficient of wind turbine profiles, employing PARSEC parametrization and random forest regression. Similarly, [PERSON] et al. [9] developed a convolutional neural network (CNN) framework for the multi-objective optimization of low Reynolds number airfoils, demonstrating the efficacy of CNNs in aerodynamic coefficient prediction, while [PERSON] et al. [10] proposed a hybrid model combining CNNs and multilayer perceptrons (MLPs) to predict flow fields around airfoils. On a different type of approach, a NASA report [11] explores the differences between graph neural networks (GNNs) and DNNs to predict aerodynamic performance for XFOIL-generated data. These studies, among others [12; 13; 14; 15; 16], are laying the groundwork for bridging classical aerodynamic theory with contemporary computational principles. To advance the current state of the research in machine learning applications for aerodynamics, this study presents a unified and methodologically transparent framework for aerodynamic performance prediction using ML. It leverages multi-modal input integration, combining high-resolution airfoil geometries, engineered geometric descriptors, and scalar flow parameters such as Reynolds number and angle of attack (AoA). These three input sources are processed through dedicated neural network branches: a CNN for geometric input and two dense neural networks (DNNs) for the scalar and engineered features. Each branch is independently tailored to extract relevant features from its respective data type, allowing for simultaneous and specialized processing before fusion. Additionally, the dataset used in this work is fully self-generated using XFOIL simulations, ensuring accessibility in how the input data were produced. One of the distinctive aspects of this work is its detailed treatment of pseudo-random number generator (PRNG) seeds.