Details
| Original language | English |
|---|---|
| Article number | 103398 |
| Journal | Advances in engineering software |
| Volume | 176 |
| Early online date | 24 Dec 2022 |
| Publication status | Published - Feb 2023 |
Abstract
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.
Keywords
- Data-driven modeling, Decision support systems (DSS), Machine learning, R shiny, Web-based platform
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
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In: Advances in engineering software, Vol. 176, 103398, 02.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Al-DeMat
T2 - A web-based expert system platform for computationally expensive models in materials design
AU - Liu, Bokai
AU - Vu-Bac, Nam
AU - Zhuang, Xiaoying
AU - Lu, Weizhuo
AU - Fu, Xiaolong
AU - Rabczuk, Timon
N1 - Funding Information: We gratefully acknowledge the support of the China Scholarship Council (CSC) .
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
KW - Data-driven modeling
KW - Decision support systems (DSS)
KW - Machine learning
KW - R shiny
KW - Web-based platform
UR - http://www.scopus.com/inward/record.url?scp=85145780170&partnerID=8YFLogxK
U2 - 10.1016/j.advengsoft.2022.103398
DO - 10.1016/j.advengsoft.2022.103398
M3 - Article
AN - SCOPUS:85145780170
VL - 176
JO - Advances in engineering software
JF - Advances in engineering software
SN - 0965-9978
M1 - 103398
ER -