深度学习
人工智能
计算机科学
机器学习
过程(计算)
进化算法
最优化问题
透视图(图形)
算法
操作系统
作者
Xiaowen Li,Lily Chang,Cao Ya-jun,Junqiang Lu,Xiaoli Lü,Hanqing Jiang
标识
DOI:10.1073/pnas.2309062120
摘要
Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.
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