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INVERSE AND FORWARD MODELING MACHINE LEARNING-BASED GENERATIVE DESIGN

机译:基于逆向和正向建模机器学习的发电设计

摘要

Machine-learned networks provide generative design. Rather than emulate the typical human design process, an inverse model is machine trained to generate a design from requirements. A simulation model is machine trained to recover performance relative to the requirements for generated designs. These two machine-trained models are used in an optimization that creates further designs from the inverse model output design and tests those designs with the simulation model. The use of machine-trained models in this loop for exploring many different designs decreases the time to explore, so may result in a more optimal design or better starting designs for the design engineer.
机译:机器学习的网络提供了生成设计。逆向模型不是经过模拟典型的人类设计过程,而是经过机器训练以根据需求生成设计。仿真模型经过机器训练,可以根据生成的设计要求恢复性能。这两个机器训练的模型用于优化中,该优化可从逆模型输出设计中创建更多设计,并使用仿真模型测试这些设计。在此循环中使用机器训练的模型来探索许多不同的设计会减少探索的时间,因此对于设计工程师而言,可能会导致更优化的设计或更好的初始设计。

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