A Physics-based Reduced Order Model with Machine Learning Boosted Hyper-Reduction.
Vlachas Konstantinos,
David Najera-Flores,
Carianne Martinez
et al.
Abstract:Physics-based Reduced Order Models (ROMs) tend to rely on projection-based reduction. This family of approaches utilizes a series of responses of the full-order model to assemble a suitable basis, subsequently employed to formulate a set of equivalent, low-order equations through projection. However, in a nonlinear setting, physics-based ROMs require an additional approximation to circumvent the bottleneck of projecting and evaluating the nonlinear contributions on the reduced space. This scheme is termed hyp… Show more
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