{"ID":2875403,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.02237","arxiv_id":"2509.02237","title":"Autoencoder-based non-intrusive model order reduction in continuum mechanics","abstract":"We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compresses high-dimensional finite element solutions into a compact latent space, (ii) a supervised regression network maps problem parameters to latent codes, and (iii) an end-to-end surrogate reconstructs full-field solutions directly from input parameters. To overcome limitations of existing approaches, we propose two key extensions: a force-augmented variant that jointly predicts displacement fields and reaction forces at Neumann boundaries, and a multi-field architecture that enables coupled field predictions, such as in thermo-mechanical systems. The framework is validated on nonlinear benchmark problems involving heterogeneous composites, anisotropic elasticity with geometric variation, and thermo-mechanical coupling. Across all cases, it achieves accurate reconstructions of high-fidelity solutions while remaining fully non-intrusive. These results highlight the potential of combining deep learning with dimensionality reduction to build efficient and extensible surrogate models. Our publicly available implementation provides a foundation for integrating data-driven model order reduction into uncertainty quantification, optimization, and digital twin applications.","short_abstract":"We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compresses high-dimensional finite element solutions into a compact latent space, (ii) a supervised regression network maps problem parameters to...","url_abs":"https://arxiv.org/abs/2509.02237","url_pdf":"https://arxiv.org/pdf/2509.02237v1","authors":"[\"Jannick Kehls\",\"Ellen Kuhl\",\"Tim Brepols\",\"Kevin Linka\",\"Hagen Holthusen\"]","published":"2025-09-02T12:05:00Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}