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Center for Nonlinear Studies |
In recent years, several sparse-regression-based techniques, such as Sparse Identification of Nonlinear Dynamics (SINDy), weak-SINDy, and operator regression methods, have been developed for system identification and surrogate modeling from data. These methods express the system dynamics as linear combinations of a prescribed set of basis functions/operators and compute the coefficients via solving a linear system built from data. In this talk, we will introduce the weak-SINDy method for constructing surrogate models, present the error analysis for weak-SINDy surrogate models, and discuss a new streaming data compression method based on weak-SINDy surrogates. This compression method utilizes the variational formulation in weak-SINDy to reduce the memory footprint during compression. Therefore, it is well-suited to be applied in the streaming scenario, in which storing the full data set offline is often infeasible.
Host: William T. Taitano (T-5)