Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Tom Bertalan"'
Autor:
Felix P. Kemeth, Tom Bertalan, Thomas Thiem, Felix Dietrich, Sung Joon Moon, Carlo R. Laing, Ioannis G. Kevrekidis
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
Machine learning tools allow to extract dynamical systems from data, however this problem remains challenging for networks and systems of interacting agents. The authors introduce an approach to learn a predictive model for the dynamics of coupled ag
Externí odkaz:
https://doaj.org/article/2920c09e87f149b2b66c83365c6a249d
Publikováno v:
Frontiers in Computational Neuroscience, Vol 14 (2020)
Systems of coupled dynamical units (e.g., oscillators or neurons) are known to exhibit complex, emergent behaviors that may be simplified through coarse-graining: a process in which one discovers coarse variables and derives equations for their evolu
Externí odkaz:
https://doaj.org/article/c7f5316eb3f8428887af011f354b66da
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
Publikováno v:
Frontiers in Computational Neuroscience, Vol 11 (2017)
Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and ultimately design. Here, we propose and illustrate a systematic and powerful approach to obtaining good co
Externí odkaz:
https://doaj.org/article/97f58c99317341fcbb3292bfcdc581d7
Autor:
Felix Dietrich, Alexei Makeev, George Kevrekidis, Nikolaos Evangelou, Tom Bertalan, Sebastian Reich, Ioannis G. Kevrekidis
Publikováno v:
Chaos: An Interdisciplinary Journal of Nonlinear Science. 33:023121
We identify effective stochastic differential equations (SDE) for coarse observables of fine-grained particle- or agent-based simulations; these SDE then provide useful coarse surrogate models of the fine scale dynamics. We approximate the drift and
Publikováno v:
ACC
We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) from spatiotemporal data. This is, of course, a classical identification
Autor:
Ioannis G. Kevrekidis, Erez Peterfreund, Felix Dietrich, Matan Gavish, Ofir Lindenbaum, Tom Bertalan, Ronald R. Coifman
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America
Significance A fundamental issue in empirical science is the ability to calibrate between different types of measurements/observations of the same phenomenon. This naturally suggests the selection of canonical variables, in the spirit of principal co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73cec8ccc5eec32c2daf865555c1f4c6
http://arxiv.org/abs/2004.07234
http://arxiv.org/abs/2004.07234
Publikováno v:
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience, Vol 14 (2020)
Frontiers in Computational Neuroscience, Vol 14 (2020)
Systems of coupled dynamical units (e.g., oscillators or neurons) are known to exhibit complex, emergent behaviors that may be simplified through coarse-graining: a process in which one discovers coarse variables and derives equations for their evolu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2c9111c734378839b70dbf7e4b5538e
Autor:
Saurabh Malani, Ioannis G. Kevrekidis, Tianqi Cui, Felix P. Kemeth, Nikolaos Evangelou, Tom Bertalan
Publikováno v:
Chaos: An Interdisciplinary Journal of Nonlinear Science. 31:093111
We present an approach, based on learning an intrinsic data manifold, for the initialization of the internal state values of long short-term memory (LSTM) recurrent neural networks, ensuring consistency with the initial observed input data. Exploitin
Publikováno v:
The European Physical Journal Special Topics. 225:1165-1180
We propose, and illustrate via a neural network example, two different approaches to coarse-graining large heterogeneous networks. Both approaches are inspired from, and use tools developed in, methods for uncertainty quantification (UQ) in systems w