Al-DeMat: A web-based expert system platform for computationally expensive models in materials design

We present a web-based framework based on the R shiny package with functional back-end server in machine learning methods. A 4-tiers architecture is programmed to achieve users’ interactive design and visualization via a web browser. Many data-driven methods are integrated into this framework, namel...

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Bibliographic Details
Published in:Advances in engineering software (1992) Vol. 176; p. 103398
Main Authors: Liu, Bokai, Vu-Bac, Nam, Zhuang, Xiaoying, Lu, Weizhuo, Fu, Xiaolong, Rabczuk, Timon
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2023
Subjects:
Computer Science
data science
Data-driven modeling
datalogi
Decision support systems (DSS)
hållfasthetslära
Machine learning
Materials Science
materialvetenskap
R shiny
Solid Mechanics
Web-based platform
Data-driven modeling
Web-based platform
Decision support systems (DSS)
R shiny
Machine learning
ISSN:0965-9978, 1873-5339
Online Access:Get full text
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Description
Summary:We present a web-based framework based on the R shiny package with functional back-end server in machine learning methods. A 4-tiers architecture is programmed to achieve users’ interactive design and visualization via a web browser. Many data-driven methods are integrated into this framework, namely Random Forest, Gradient Boosting Machine, Artificial and Deep neural networks. Moreover, a robust gradient-free optimization technique, the Particle Swarm Optimization, is used to search optimal values in hyper-parameters tuning. K-fold Cross Validation is applied to avoid over-fitting. R2 and RMSE are considered as two key factors to evaluate the trained models. The contributions to the expert system in materials design are: (1) A systematic framework that can be applied in materials prediction with machine learning approaches, (2) A user-friendly web-based platform that is easy and flexible to use and (3) integrated optimization and visualization into the framework with pre set algorithms. This computational framework is designed for researchers and materials engineers who would like to do the preliminary designs before experimental studies. Finally, we demonstrate the performance of the web-based framework through 2 case studies. •A web-based framework is presented based on the R shiny package with functional back-end server in machine learning methods.•A 4-tiers architecture is programmed with many data-driven methods to achieve users’ interactive design and visualization via a web browser.•A systematic framework that can be applied in materials prediction with machine learning approaches.•A user-friendly web-based platform that is easy and flexible to operate.•Optimization and visualization are Integrated into the framework with preset algorithms.
ISSN:0965-9978
1873-5339
DOI:10.1016/j.advengsoft.2022.103398