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- W2887661471 abstract "Developing physically interpretable reduced-order models (ROMs) is critical as they provide an understanding of the underlying phenomena apart from computational tractability for many chemical processes. In this work, we re-envision the model reduction of nonlinear dynamical systems from the perspective of regression. In particular, we solve a sparse regression problem over a large set of candidate functional forms to determine the structure of the ROM. The method balances model complexity and accuracy by selecting a sparse model that avoids overfitting to accurately represent the system dynamics when subjected to a different input profile. By applying to a hydraulic fracturing process, we demonstrate the ability of the developed models to reveal important physical phenomena such as proppant transport and fracture propagation inside a fracture. It also highlights how a priori knowledge can be incorporated easily into the algorithm and results in accurate ROMs that are used for controller synthesis." @default.
- W2887661471 created "2018-08-22" @default.
- W2887661471 creator A5028617759 @default.
- W2887661471 creator A5057131647 @default.
- W2887661471 date "2018-11-01" @default.
- W2887661471 modified "2023-10-16" @default.
- W2887661471 title "Data-driven identification of interpretable reduced-order models using sparse regression" @default.
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- W2887661471 doi "https://doi.org/10.1016/j.compchemeng.2018.08.010" @default.
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