首页|University of Tokyo Researcher Adds New Study Findings to Research in Artificial Intelligence (Hypervolume-Based Multi- Objective Optimization Method Applying De ep Reinforcement Learning to the Optimization of Turbine Blade Shape)
University of Tokyo Researcher Adds New Study Findings to Research in Artificial Intelligence (Hypervolume-Based Multi- Objective Optimization Method Applying De ep Reinforcement Learning to the Optimization of Turbine Blade Shape)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, “A multi-objective turbine shape optimizatio n method based on deep reinforcement learning (DRL) is proposed.” Financial supporters for this research include Jsps Kakenhi. The news reporters obtained a quote from the research from University of Tokyo: “DRL-based optimization methods are useful for repeating optimization tasks that arise in applications such as the design of turbines and automotive parts. In c onventional research, DRL is applied only to single-optimization tasks. In this study, a multi-objective optimization method using improvements in hypervolume i s proposed.” According to the news editors, the research concluded: “The proposed method is a pplied to a benchmark problem and a turbine optimization problem. It succeeded i n efficiently solving the problems, and Pareto optimal solutions are obtained.”
University of TokyoTokyoJapanAsiaArtificial IntelligenceEmerging TechnologiesMachine LearningReinforcement Learning
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