Zobrazeno 1 - 10
of 300
pro vyhledávání: '"Erik M. Bollt"'
Autor:
Neranjaka Jayarathne, Erik M. Bollt
Publikováno v:
AIMS Mathematics, Vol 9, Iss 1, Pp 998-1022 (2024)
Reduced order modelling relies on representing complex dynamical systems using simplified modes, which can be achieved through the Koopman operator(KO) analysis. However, computing Koopman eigenpairs for high-dimensional observable data can be ineffi
Externí odkaz:
https://doaj.org/article/bebaebe20ace4bf6877d9adf089505c4
Autor:
James P. Bagrow, Erik M. Bollt
Publikováno v:
Applied Network Science, Vol 4, Iss 1, Pp 1-15 (2019)
Abstract As network research becomes more sophisticated, it is more common than ever for researchers to find themselves not studying a single network but needing to analyze sets of networks. An important task when working with sets of networks is net
Externí odkaz:
https://doaj.org/article/3dcbcd6cf4624cccac8bd623a85c9e9f
Publikováno v:
Northeast Journal of Complex Systems, Vol 3, Iss 1 (2021)
The presence of hierarchy in many real-world networks is not yet fully understood. We observe that complex interaction networks are often coarse-grain models of vast modular networks, where tightly connected subgraphs are agglomerated into nodes for
Externí odkaz:
https://doaj.org/article/9866b6d0899c4450aa6929de2e07a50a
Publikováno v:
SIAM Journal on Applied Dynamical Systems. 21:2642-2696
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad24b5b8951b2bbf7ac14eef78dab07e
https://doi.org/10.1137/21m1464300
https://doi.org/10.1137/21m1464300
Autor:
Felix P. Kemeth, Sindre W. Haugland, Felix Dietrich, Tom Bertalan, Kevin Hohlein, Qianxiao Li, Erik M. Bollt, Ronen Talmon, Katharina Krischer, Ioannis G. Kevrekidis
Publikováno v:
IEEE Access, Vol 6, Pp 77402-77413 (2018)
Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case
Externí odkaz:
https://doaj.org/article/9d795c384df047b186d273cf5d16fed1
Autor:
Sudam Surasinghe, Erik M. Bollt
Publikováno v:
Mathematics, Vol 9, Iss 21, p 2803 (2021)
A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a redu
Externí odkaz:
https://doaj.org/article/55dee2a3996c4ff5b281e86122c02833
Autor:
Erik M. Bollt, Shane D. Ross
Publikováno v:
Mathematics, Vol 9, Iss 21, p 2731 (2021)
This work serves as a bridge between two approaches to analysis of dynamical systems: the local, geometric analysis, and the global operator theoretic Koopman analysis. We explicitly construct vector fields where the instantaneous Lyapunov exponent f
Externí odkaz:
https://doaj.org/article/30f66abf12b648debe51bb17f53fbb57
Autor:
Sudam Surasinghe, Erik M. Bollt
Publikováno v:
Entropy, Vol 22, Iss 4, p 396 (2020)
Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts
Externí odkaz:
https://doaj.org/article/a4d6055da73840e2999e5fe7a41cdcce
Autor:
Erik M. Bollt, Maria Lombardi, Carmela Calabrese, Benoît G. Bardy, Mario di Bernardo, Pietro De Lellis
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Scientific Reports
Scientific Reports
Synchronization of human networks is fundamental in many aspects of human endeavour. Recently, much research effort has been spent on analyzing how motor coordination emerges in human groups (from rocking chairs to violin players) and how it is affec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::465c41b360e9def0f5752aebaa8525d7
https://doaj.org/article/14f57a0fcd6c4312a7c38574533cc562
https://doaj.org/article/14f57a0fcd6c4312a7c38574533cc562
Publikováno v:
Journal of Advances in Mathematics and Computer Science. :66-83
Full-waveform inversion (FWI) is a non-destructive health monitoring technique that can be used to identify and quantify the embedded anomalies. The forward modeling of the FWI consists of a simulation of elastic wave equation to generate synthetic d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a6dc4f86f7cc99b3fd9214e7cfd3cc91
https://doi.org/10.9734/jamcs/2021/v36i530364
https://doi.org/10.9734/jamcs/2021/v36i530364