首页|Data from University of Kyrenia Advance Knowledge in Machine Learning (Airfoil a erodynamic performance prediction using machine learning and surrogate modeling)
Data from University of Kyrenia Advance Knowledge in Machine Learning (Airfoil a erodynamic performance prediction using machine learning and surrogate modeling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Investigators publish new report on artificial in telligence. According to news reporting from theUniversity of Kyrenia by NewsRx journalists, research stated, “In recent times, machine learning algorithmshav e gained significant traction in addressing aerodynamic challenges.”The news journalists obtained a quote from the research from University of Kyren ia: “These algorithmsprove invaluable for predicting the aerodynamic performanc e, specifically the Lift-to-Drag ratio of airfoildatasets, when the dataset is sufficiently large and diverse. In this paper, we delve into an exploration of five machine learning algorithms: Random Forest, Gradient Boosting Regression, De cision Tree Regressor,AdaBoost Algorithm, and Linear Regression. These algorith ms are scrutinized within the context of varioustrain/test ratios to predict a crucial aerodynamic performance metric-the lift-to-drag ratio-for differentangl e of attack values. Our evaluation encompasses an array of metrics including R2, Mean Square Error,Training time, and Evaluation time. Upon analysis, the Rando m Forest Method, with a train/test ratioof 0.2, emerges as the frontrunner, sho wcasing superior predictive performance when compared to itscounterparts.”
University of KyreniaAlgorithmsCybor gsEmerging TechnologiesMachine Learning
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