A data-driven multiscale scheme for anisotropic finite strain magneto-elasticity

📝 Original Info

• Title: A data-driven multiscale scheme for anisotropic finite strain magneto-elasticity
• ArXiv ID: 2510.24197
• Date: 2025-10-28
• Authors: ** - 논문에 명시된 저자 정보가 제공되지 않았습니다. (원문에서 저자명을 확인하시기 바랍니다.) **

📝 Abstract

In this work, we develop a neural network-based, data-driven, decoupled multiscale scheme for the modeling of structured magnetically soft magnetorheological elastomers (MREs). On the microscale, sampled magneto-mechanical loading paths are imposed on a representative volume element containing spherical particles and an elastomer matrix, and the resulting boundary value problem is solved using a mixed finite element formulation. The computed microscale responses are homogenized to construct a database for the training and testing of a macroscopic physics-augmented neural network model. The proposed model automatically detects the material's preferred direction during training and enforces key physical principles, including objectivity, material symmetry, thermodynamic consistency, and the normalization of free energy, stress, and magnetization. Within the range of the training data, the model enables accurate predictions of magnetization, mechanical stress, and total stress. For larger magnetic fields, the model yields plausible results. Finally, we apply the model to investigate the magnetostrictive behavior of a macroscopic spherical MRE sample, which exhibits contraction along the magnetic field direction when aligned with the material's preferred direction.

💡 Deep Analysis

📄 Full Content

Reference