Multi-Objective Automated Machine Learning for Inversion of Mesoscopic Parameters in Discrete Element Contact Models

Multi-Objective Automated Machine Learning for Inversion of Mesoscopic Parameters in Discrete Element Contact Models

Accurate calibration of mesoscopic contact model parameters is essential for ensuring the reliability of Particle Flow Code in Three Dimensions (PFC3D) simulations in geotechnical engineering. Trial-and-error approaches are often used to determine the parameters of the contact model, but they are ti...

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Bibliographic Details
Main Authors: Xu Ao, Shengpeng Hao, Yuyu Zhang, Wenyu Xu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
mesoscopic parameter inversion
PFC3D simulations
automated machine learning (AutoML)
uniaxial compression tests
linear parallel bond contact model
Online Access:https://www.mdpi.com/2076-3417/15/15/8181
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