Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures
arXiv:2606.00938v1 Announce Type: cross Abstract: Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials. In this contribution, a supervised learning approach is presented for predicting effective Lam\'e parameters of hyperelastic composites from low-dimensional microstructural descriptors. The data set is based on previously published numerical homogenization results for ensembles of two-phase stochastic microstructures generated by planar Boolean models, covering variations of inclusion shape, phase contrast, and area fraction; see Br\"andel, B
The increasing availability of computational power and advanced machine learning techniques makes data-driven surrogate modeling for complex material science problems more feasible and efficient.
This development offers a significant acceleration in materials design and engineering, potentially reducing the time and cost associated with developing new high-performance materials.
Material scientists and engineers can now more rapidly predict material properties without extensive numerical simulations, enabling faster innovation cycles in various industries.
- · Materials science and engineering
- · Manufacturing sector
- · AI/ML research institutions
- · Aerospace and automotive industries
- · Traditional numerical simulation firms
Reduced lead times and costs in new material development.
Acceleration of research and development in fields reliant on advanced materials.
Enhanced material performance characteristics leading to more efficient products and designs across multiple sectors.
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