New Fluids Research Study Results Reported from China Academy of Aerospace Aerodynamics (Multi-fidelity Convolutional Neural Network Surrogate Model for Aerodynamic Optimization Based On Transfer Learning)

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New Fluids Research Study Results Reported from China Academy of Aerospace Aerodynamics (Multi-fidelity Convolutional Neural Network Surrogate Model for Aerodynamic Optimization Based On Transfer Learning)

机译：新的流体研究结果报告中国航天空气动力(Multi-fidelity卷积神经网络代理模型气动优化基于传输的学习)

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By a News Reporter-Staff News Editor at Network Daily News – Data detailed on Fluids Research have been presented. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “In aerodynamic shape optimization, a high-fidelity (HF) simulation is generally more accurate but more time-consuming than a low-fidelity (LF) simulation. To take advantage of both HF and LF simulations, a multi-fidelity convolutional neural network (CNN) surrogate model with transfer learning (MFCNN-TL) is proposed, which integrates different fidelity information through fine-tuning and adaptively learns their nonlinear mapping.”

机译：由一个新闻记者在网络新闻编辑每日新闻-数据详细的流体研究已经提出。北京,中华人民共和国NewsRx记者,研究表示,“在符合空气动力学的形状优化,高保真(高频)仿真通常是更准确的更耗时的低保真(低频)模拟。模拟,multi-fidelity卷积神经网络(CNN)代理模型传输学习(MFCNN-TL)的提议,该提议通过集成不同保真度信息微调和自适应地学习他们的非线性映射”。

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《Network Daily News》 |2022年第18期|12-12|共1页
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正文语种英语
中图分类 TP-778;
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Neural network; Beijing; Outcome StudiesChinaInventionsnewsTransfer (Psychology)NetworkFluidAsiaArtificial IntelligenceMachine Learning;

机译：神经网络;北京;结果StudiesChinaInventionsnewsTransfer(心理学)NetworkFluidAsiaArtificialIntelligenceMachine学习;

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New Fluids Research Study Results Reported from China Academy of Aerospace Aerodynamics (Multi-fidelity Convolutional Neural Network Surrogate Model for Aerodynamic Optimization Based On Transfer Learning)

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