Daniel Dylewsky
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Explore the profile of Daniel Dylewsky including associated specialties, affiliations and a list of published articles.
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6
Citations
19
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Recent Articles
1.
Dylewsky D, Anand M, Bauch C
Sci Rep
. 2024 Aug;
14(1):18277.
PMID: 39107398
Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional...
2.
Bury T, Dylewsky D, Bauch C, Anand M, Glass L, Shrier A, et al.
Nat Commun
. 2023 Oct;
14(1):6331.
PMID: 37816722
Many natural and man-made systems are prone to critical transitions-abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning...
3.
Dylewsky D, Lenton T, Scheffer M, Bury T, Fletcher C, Anand M, et al.
J R Soc Interface
. 2023 Apr;
20(201):20220562.
PMID: 37015262
The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using...
4.
Dylewsky D, Kaiser E, Brunton S, Kutz J
Phys Rev E
. 2022 Feb;
105(1-2):015312.
PMID: 35193205
Delay embeddings of time series data have emerged as a promising coordinate basis for data-driven estimation of the Koopman operator, which seeks a linear representation for observed nonlinear dynamics. Recent...
5.
Bramburger J, Dylewsky D, Kutz J
Phys Rev E
. 2020 Sep;
102(2-1):022204.
PMID: 32942395
Multiscale phenomena that evolve on multiple distinct timescales are prevalent throughout the sciences. It is often the case that the governing equations of the persistent and approximately periodic fast scales...
6.
Dylewsky D, Tao M, Kutz J
Phys Rev E
. 2019 Jul;
99(6-1):063311.
PMID: 31330631
We present a data-driven method for separating complex, multiscale systems into their constituent timescale components using a recursive implementation of dynamic mode decomposition (DMD). Local linear models are built from...