Building surrogate models for engineering problems by integrating limited simulation data and monotonic engineering knowledge. (August 2021)

• Record Type: Journal Article
• Authors: Hao, Jia; Ye, Wenbin; Jia, Liangyue; Wang, Guoxin; Allen, Janet
• Is Part Of: Advanced engineering informatics. Volume 49(2021)
• Publication Date: 2021-08
• Subjects: Surrogate model -- Limited simulation data -- Engineering knowledge -- Evolutionary neural network -- Neuro Evolution of augmenting topologies (NEAT); Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101342
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Defining monotonic engineering knowledge. A construction method of multi-objective evolutionary neural network. Integrating engineering knowledge into the surrogate model. Twelve experiments are conducts to validate the method proposed. Abstract: The use of surrogate models to replace expensive computations with computer simulations has been widely studied in engineering problems. However, often only limited simulation data is available when designing complex products due to the cost of obtaining this kind of data. This presents a challenge for building surrogate models because the information contained in the limited simulation data is incomplete. Therefore, a method for building surrogate models by integrating limited simulation da ta and engineering know ledge with e volutionary neural networks (eDaKnow) is presented. In eDaKnow, a neural network uses an evolutionary algorithm to integrate the simulation data and the monotonic engineering knowledge to learn its weights and structure synchronously. This method involves converting both limited simulation data and engineering knowledge into the respective fitness functions. Compared with the previous work of others, we propose a method to train the surrogate model by combining data and knowledge through evolutionary neural network. We take knowledge as fitness function to train the model, and use a network structure self-learning method, which means that there is no need to adjust the network structure manually. The empirical results show that: (1) eDaKnow can be used to integrate limited simulation data and monotonic knowledge into a neural network, (2) the prediction accuracy of the newly constructed surrogate model is increased significantly, and (3) the proposed eDaKnow outperforms other methods on relatively complex benchmark functions and engineering problems.