  This paper proposes a novel Adaptive Clustering-based Reduced-Order Modeling
(ACROM) framework to significantly improve and extend the recent family of
clustering-based reduced-order models (CROMs). This adaptive framework enables
the clustering-based domain decomposition to evolve dynamically throughout the
problem solution, ensuring optimum refinement in regions where the relevant
fields present steeper gradients. It offers a new route to fast and accurate
material modeling of history-dependent nonlinear problems involving highly
localized plasticity and damage phenomena. The overall approach is composed of
three main building blocks: target clusters selection criterion, adaptive
cluster analysis, and computation of cluster interaction tensors. In addition,
an adaptive clustering solution rewinding procedure and a dynamic adaptivity
split factor strategy are suggested to further enhance the adaptive process.
The coined Adaptive Self-Consistent Clustering Analysis (ASCA) is shown to
perform better than its static counterpart when capturing the multi-scale
elasto-plastic behavior of a particle-matrix composite and predicting the
associated fracture and toughness. Given the encouraging results shown in this
paper, the ACROM framework sets the stage and opens new avenues to explore
adaptivity in the context of CROMs.
