Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Gear, C. William"'
Autor:
Holiday, Alexander, Kooshkbaghi, Mahdi, Bello-Rivas, Juan M., Gear, C. William, Zagaris, Antonios, Kevrekidis, Ioannis G.
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that reduces complexity and facilitates exploration of behavioral aspects of
Externí odkaz:
http://arxiv.org/abs/1807.08338
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 c
Externí odkaz:
http://arxiv.org/abs/1705.00104
Autor:
Chiavazzo, Eliodoro, Coifman, Ronald R., Covino, Roberto, Gear, C. William, Georgiou, Anastasia S., Hummer, Gerhard, Kevrekidis, Ioannis G.
We describe and implement iMapD, a computer-assisted approach for accelerating the exploration of uncharted effective Free Energy Surfaces (FES), and more generally for the extraction of coarse-grained, macroscopic information from atomistic or stoch
Externí odkaz:
http://arxiv.org/abs/1701.01513
Autor:
Dsilva, Carmeline J., Talmon, Ronen, Gear, C. William, Coifman, Ronald R., Kevrekidis, Ioannis G.
Multiple time scale stochastic dynamical systems are ubiquitous in science and engineering, and the reduction of such systems and their models to only their slow components is often essential for scientific computation and further analysis. Rather th
Externí odkaz:
http://arxiv.org/abs/1501.05195
In recent years, individual-based/agent-based modeling has been applied to study a wide range of applications, ranging from engineering problems to phenomena in sociology, economics and biology. Simulating such agent-based models over extended spatio
Externí odkaz:
http://arxiv.org/abs/1404.7199
Autor:
Chiavazzo, Eliodoro, Gear, C. William, Dsilva, Carmeline J., Rabin, Neta, Kevrekidis, Ioannis G.
The adoption of detailed mechanisms for chemical kinetics often poses two types of severe challenges: First, the number of degrees of freedom is large; and second, the dynamics is characterized by widely disparate time scales. As a result, reactive f
Externí odkaz:
http://arxiv.org/abs/1307.6849
We link nonlinear manifold learning techniques for data analysis/compression with model reduction techniques for evolution equations with time scale separation. In particular, we demonstrate a `"nonlinear extension" of the POD-Galerkin approach to ob
Externí odkaz:
http://arxiv.org/abs/1011.5197
The long time behavior of the dynamics of a fast-slow system of ordinary differential equations is examined. The system is derived from a spatial discretization of a Korteweg-de Vries-Burgers type equation, with fast dispersion and slow diffusion. Th
Externí odkaz:
http://arxiv.org/abs/0908.2752
Autor:
Kavousanakis, Michail E., Erban, Radek, Boudouvis, Andreas G., Gear, C. William, Kevrekidis, Ioannis G.
Temporal integration of equations possessing continuous symmetries (e.g. systems with translational invariance associated with traveling solutions and scale invariance associated with self-similar solutions) in a ``co-evolving'' frame (i.e. a frame w
Externí odkaz:
http://arxiv.org/abs/math/0608122
We demonstrate how direct simulation of stochastic, individual-based models can be combined with continuum numerical analysis techniques to study the dynamics of evolving diseases. % Sidestepping the necessity of obtaining explicit population-level m
Externí odkaz:
http://arxiv.org/abs/nlin/0310011