.png){kind=icon}
Return
Physics-supervised deep learning-based optimization (PSDLO) with accuracy and efficiency
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.2309062120.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
P
IF:
9.1
Papers:
10.8W
Citations:
73.5W
Organization
Cited Papers
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.png)
NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveShareSaveEvolving genetic programming models of higher generalization ability in modelling of turning process
PREAI
ShareSaveAn overview of applicability of WEQ, RWEQ, and WEPS models for prediction of wind erosion in lands
PREAI
ShareSaveDeep learning model to predict complex stress and strain fields in hierarchical compositesSCIENCE ADVANCESOAAI
SCIENCE ADVANCESIF12.5
OAAIShareSave
View more