Physics-supervised deep learning-based optimization (PSDLO) with accuracy and efficiency

doi:10.1073/pnas.2309062120

Physics-supervised deep learning-based optimization (PSDLO) with accuracy and efficiency

2023-08-21

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DOI：10.1073/pnas.2309062120

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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.

Keywords:

physics-supervise

deep learning

evolutionary algorithm

accuracy

efficiency

Physics-supervised deep learning-based optimization (PSDLO) with accuracy and efficiency

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