首页|University of California Researcher Publishes Findings in Machine Learning (Simu lation-Based Optimization for Vertiport Location Selection: A Surrogate Model Wi th Machine Learning Method)
University of California Researcher Publishes Findings in Machine Learning (Simu lation-Based Optimization for Vertiport Location Selection: A Surrogate Model Wi th Machine Learning Method)
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New study results on artificial intell igence have been published. According to news reporting originating from Berkele y, California, by NewsRx correspondents, research stated, "We present Vertiport- informed Surrogate-Based Optimization with Machine Learning Surrogates (VinS), a novel framework for solving the vertiport location problem for urban air mobili ty operations." Our news journalists obtained a quote from the research from University of Calif ornia: "The primary focus of this work is on the optimization of vertiport locat ions to facilitate efficient urban air transportation. Our framework helps choos e not only the optimal vertiport locations but also the optimal number of vertip orts. We develop a simulation model to analyze the impacts of various vertiport location configurations on the efficiency of the transportation network. To expe dite the simulation process, a surrogate model is trained using machine learning algorithms, effectively reducing the computational time for evaluating a given vertiport location configuration. With the machine learning surrogate models, we apply a genetic algorithm to find the optimal set of vertiport locations."
University of CaliforniaBerkeleyCali forniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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