CEBoosting: Online Sparse Identification of Dynamical Systems with
Regime Switching by Causation Entropy Boosting
- URL: http://arxiv.org/abs/2304.07863v1
- Date: Sun, 16 Apr 2023 19:14:03 GMT
- Title: CEBoosting: Online Sparse Identification of Dynamical Systems with
Regime Switching by Causation Entropy Boosting
- Authors: Chuanqi Chen, Nan Chen, Jin-Long Wu
- Abstract summary: Regime switching is ubiquitous in many complex dynamical systems with multiscale features, chaotic behavior, and extreme events.
In this paper, a causation entropy boosting (CEBoosting) strategy is developed to facilitate the detection of regime switching.
The strategy can be combined with data assimilation to identify regime switching triggered by the unobserved latent processes.
- Score: 1.090458267119282
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Regime switching is ubiquitous in many complex dynamical systems with
multiscale features, chaotic behavior, and extreme events. In this paper, a
causation entropy boosting (CEBoosting) strategy is developed to facilitate the
detection of regime switching and the discovery of the dynamics associated with
the new regime via online model identification. The causation entropy, which
can be efficiently calculated, provides a logic value of each candidate
function in a pre-determined library. The reversal of one or a few such
causation entropy indicators associated with the model calibrated for the
current regime implies the detection of regime switching. Despite the short
length of each batch formed by the sequential data, the accumulated value of
causation entropy corresponding to a sequence of data batches leads to a robust
indicator. With the detected rectification of the model structure, the
subsequent parameter estimation becomes a quadratic optimization problem, which
is solved using closed analytic formulae. Using the Lorenz 96 model, it is
shown that the causation entropy indicator can be efficiently calculated, and
the method applies to moderately large dimensional systems. The CEBoosting
algorithm is also adaptive to the situation with partial observations. It is
shown via a stochastic parameterized model that the CEBoosting strategy can be
combined with data assimilation to identify regime switching triggered by the
unobserved latent processes. In addition, the CEBoosting method is applied to a
nonlinear paradigm model for topographic mean flow interaction, demonstrating
the online detection of regime switching in the presence of strong
intermittency and extreme events.
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