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Reduced Order Modeling for systems with component nonlinearities
(2021)Deriving digital twins of real-life dynamical systems is an intricate modeling task. These representations have gained significance in the current virtualization era, thus bringing Reduced Order Models (ROMs) in the foreground. An efficient low-order representation should capture nonlinear effects, while retaining any dependencies of the real-life system over system- and loading-related traits. Several schemes have been proposed in this ...Other Conference Item -
A Machine Learning Framework for Alleviating Bottlenecks of Projection-Based Reduced Order Models
(2021)Other Conference Item