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A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling
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DOI：10.1016/j.cma.2019.112657.png)
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Abstract
En 中文
Developing an accurate nonlinear reduced order model from simulation data has been an outstanding research topic for many years. For many physical systems, data collection is very expensive and the optimal data distribution is not known in advance. Thus, maximizing the information gain remains a grand challenge. In a recent paper, Bhattacharjee and Matous (2016) proposed a manifold-based nonlinear reduced order model for multiscale problems in mechanics of materials. Expanding this work here, we develop a novel sampling strategy based on the physics/pattern-guided data distribution. Our adaptive sampling strategy relies on enrichment of sub-manifolds based on the principal stretches and rotational sensitivity analysis. This novel sampling strategy substantially decreases the number of snapshots needed for accurate reduced order model construction (i.e., similar to 5x reduction of snapshots over Bhattacharjee and Matous (2016)). Moreover, we build the nonlinear manifold using the displacement rather than deformation gradient data. We provide rigorous verification and error assessment. Finally, we demonstrate both localization and homogenization of the multiscale solution on a large particulate composite unit cell. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Computational homogenization
Nonlinear manifold
Reduced order model
Machine learning
Parallel computing
Big data
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