Skip to Main content Skip to Navigation
Journal articles

Surrogate Model via Artificial Intelligence Method for Accelerating Screening Materials and Performance Prediction

Abstract : Predicting the performance of mechanical properties is an important and current issue in the field of engineering and materials science, but traditional experiments and modeling calculations often consume large amounts of time and resources. Therefore, it is imperative to use appropriate methods to accelerate the process of material selection and design. The artificial intelligence method, particularly deep learning models, has been verified as an effective and efficient method for handling computer vision and neural language problems. In this paper, a deep learning surrogate model (DLS) is proposed for predicting the mechanical performance of materials, that is, the maximum stress value under complex working conditions. The DLS can reproduce the finite element analysis model results with 98.79% accuracy. The results show that deep learning has great potential. This research also provides a new approach for material screening in practical engineering.
Document type :
Journal articles
Complete list of metadata

https://hal-utt.archives-ouvertes.fr/hal-03320764
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 1:38:45 PM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

Identifiers

Collections

UTT

Citation

Tian Wang, Mingqi Shao, Rong Guo, Fei Tao, Gang Zhang, et al.. Surrogate Model via Artificial Intelligence Method for Accelerating Screening Materials and Performance Prediction. Advanced Functional Materials, Wiley, 2021, 31 (8), pp.2006245. ⟨10.1002/adfm.202006245⟩. ⟨hal-03320764⟩

Share

Metrics

Record views

11