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C. William Gear
Researcher at Princeton University
Publications - 28
Citations - 2408
C. William Gear is an academic researcher from Princeton University. The author has contributed to research in topics: State variable & Nonlinear dimensionality reduction. The author has an hindex of 16, co-authored 28 publications receiving 2192 citations.
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Equation-Free, Coarse-Grained Multiscale Computation: Enabling Mocroscopic Simulators to Perform System-Level Analysis
C. William Gear,James M. Hyman,Panagiotis G Kevrekidid,Ioannis G. Kevrekidis,Olof Runborg,Constantinos Theodoropoulos +5 more
TL;DR: A framework for computer-aided multiscale analysis, which enables models at a fine (microscopic/stochastic) level of description to perform modeling tasks at a coarse (macroscopic, systems) level, and can bypass the derivation of the macroscopic evolution equations when these equations conceptually exist but are not available in closed form is presented.
Journal ArticleDOI
Equation-free: The computer-aided analysis of complex multiscale systems
TL;DR: Over the last few years with several collaborators, a mathematically inspired, computational enabling technology is developed and validated that allows the modeler to perform macroscopic tasks acting on the microscopic models directly, and can lead to experimental protocols for the equation-free exploration of complex system dynamics.
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The index of general nonlinear DAEs
TL;DR: In this paper, the authors define the maximum indices, which are the maxima of earlier indices in a neighborhood of the solution over a set of perturbations, and show that these indices are simply not related to each other.
Posted Content
Equation-Free Multiscale Computation: enabling microscopic simulators to perform system-level tasks
Ioannis G. Kevrekidis,C. William Gear,James M. Hyman,Panagiotis G. Kevrekidis,Olof Runborg,Constantinos Theodoropoulos +5 more
TL;DR: A framework for computer-aided multiscale analysis, which enables models at a "fine" (microscopic/stochastic) level of description to perform modeling tasks at a 'coarse' (macroscopic, systems) level, and can bypass the derivation of the macroscopic evolution equations when these equations conceptually exist but are not available in closed form.
Journal ArticleDOI
The gap-tooth method in particle simulations
TL;DR: In this paper, the gap-to-teeth method is used for multiscale modeling of systems represented by microscopic physics-based simulators, when coarse-grained evolution equations are not available in closed form.