A new machine learning based method for sampling virtual experiments and its effect on the parameter identification for anisotropic yield models

Wessel, Alexander; Morand, Lukas; Butz, Alexander; Helm, Dirk; Volk, Wolfram

doi:10.1088/1757-899x/1157/1/012026

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…The virtual training environment is built on the basis of the real environment, and the operating rules are also based on the actual operating specifications. The ideal virtual environment can even make it difficult for users to distinguish the true from the false [4]. Virtual drill breaks the limitation of drill space, trainees can conduct centralized drills in any geographical environment, and users can enter the same virtual drill site for real-time centralized drills through relevant network communication equipment.…”

Section: Figure 1 Basic Elements Of a Virtual Reality Systemmentioning

confidence: 99%

Discussion on the Application of Virtual Reality Technology in Pharmacy Vocational Education

Zhang¹

2023

Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)

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With the rapid development of virtual reality technology in recent years, the virtual images in the virtual reality system can deeply interact with users. Virtual reality technology is now entering colleges and universities and has become a tool for many experimental teaching. Virtual reality technology has played an important role in pharmacy vocational education. This paper analyzes the benefits brought by virtual reality technology to pharmacy vocational education, and designs a virtual experiment teaching system based on B/S mode. The teaching system in this paper allows students to use computer equipment to learn online and choose to operate various experiments, so as to improve the efficiency and quality of experimental teaching.

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Section: Figure 1 Basic Elements Of a Virtual Reality Systemmentioning

confidence: 99%

Discussion on the Application of Virtual Reality Technology in Pharmacy Vocational Education

Zhang¹

2023

Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)

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“…By virtual testing procedures, the deformation behavior can be analyzed on the basis of the Finite Element Method (FEM) [78] or fast Fourier transform (FFT) [79]. The advantage of using RVEs is that arbitrary load cases can be applied, which are often hard to apply in real experiments, [80][81][82]. The approach to calibrate macroscopic material models to RVE data can be called virtual laboratory [2].…”

Section: Aspects Of Materials Modelingmentioning

confidence: 99%

Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics

Dornheim,

Morand,

Nallani

et al. 2023

Arch Computat Methods Eng

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Analyzing and modeling the constitutive behavior of materials is a core area in materials sciences and a prerequisite for conducting numerical simulations in which the material behavior plays a central role. Constitutive models have been developed since the beginning of the 19th century and are still under constant development. Besides physics-motivated and phenomenological models, during the last decades, the field of constitutive modeling was enriched by the development of machine learning-based constitutive models, especially by using neural networks. The latter is the focus of the present review paper, which aims to give an overview of neural networks-based constitutive models from a methodical perspective. The review summarizes and compares numerous conceptually different neural networks-based approaches for constitutive modeling including neural networks used as universal function approximators, advanced neural network models and neural network approaches with integrated physical knowledge. The upcoming of these methods is in-turn closely related to advances in the area of computer sciences, what further adds a chronological aspect to this review. We conclude the review paper with important challenges in the field of learning constitutive relations that need to be tackled in the near future.

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Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

Shoghi,

Morand,

Helm

et al. 2024

Comput Mech

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In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating constitutive models straight from data, capable of describing complex material behavior in a more flexible way than classical constitutive models. Yield functions, which serve as foundation of constitutive models for plasticity, can be properly described in a data-oriented manner using ML methods. However, the quality of these descriptions heavily relies on the availability of sufficient high-quality and representative training data that needs to be generated by fundamental numerical simulations, experiments, or a combination of both. The present paper addresses the issue of data selection, by introducing an active learning approach for Support Vector Classification (SVC) and its application in training an ML yield function with suitable data. In this regard, the Query-By-Committee (QBC) algorithm was employed, guiding the selection of new training data points in regions of the feature space where a committee of models shows significant disagreement. This approach resulted in a marked reduction in the variance of model predictions throughout the active learning process. It was also shown that the rate of decrease in the variance went along with an increase in the quality of the trained model, quantified by the Matthews Correlation Coefficient (MCC). This demonstrated the effectiveness of the approach and offered us the possibility to define a dynamic stopping criterion based on the variance in the committee results.

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A new machine learning based method for sampling virtual experiments and its effect on the parameter identification for anisotropic yield models

Cited by 5 publications

References 25 publications

Discussion on the Application of Virtual Reality Technology in Pharmacy Vocational Education

Discussion on the Application of Virtual Reality Technology in Pharmacy Vocational Education

Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics

Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

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