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nonlinear manifold

karelmatous's picture

A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling

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.

karelmatous's picture

A nonlinear manifold-based reduced order model

A new perspective on model reduction for nonlinear multi-scale analysis of heterogeneous materials. In this work, we seek meaningful low-dimensional structures hidden in high-dimensional multi-scale data.

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