Efficient computational homogenization via tensor train format
<|reference_start|>Efficient computational homogenization via tensor train format: Real-world physical systems, like composite materials and porous media, exhibit complex heterogeneities and multiscale nature, posing significant computational challenges. Computational homogenization is useful for predicting macroscopic properties from the microscopic material constitution. It involves defining a representative volume element (RVE), solving governing equations, and evaluating its properties such as conductivity and elasticity. Despite its effectiveness, the approach can be computationally expensive. This study proposes a tensor-train (TT)-based asymptotic homogenization method to address these challenges. By deriving boundary value problems at the microscale and expressing them in the TT format, the proposed method estimates material properties efficiently. We demonstrate its validity and effectiveness through numerical experiments applying the proposed method for homogenization of thermal conductivity and elasticity in two- and three-dimensional materials, offering a promising solution for handling the multiscale nature of heterogeneous systems.<|reference_end|>
@article{sato2024efficient,
    title={Efficient computational homogenization via tensor train format},
    author={Yuki Sato, Yuto Lewis Terashima, Ruho Kondo},
    journal={arXiv preprint arXiv:2407.18870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18870},
    primaryClass={cs.CE}
}