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Equation-free: The computer-aided analysis of complex multiscale systems

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TLDR
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.
Abstract
The best available descriptions of systems often come at a fine level (atomistic, stochastic, microscopic, agent based), whereas the questions asked and the tasks required by the modeler (prediction, parametric analysis, optimization, and control) are at a much coarser, macroscopic level. Traditional modeling approaches start by deriving macroscopic evolution equations from microscopic models, and then bringing an arsenal of computational tools to bear on these macroscopic descriptions. Over the last few years with several collaborators, we have developed and validated a mathematically inspired, computational enabling technology that allows the modeler to perform macroscopic tasks acting on the microscopic models directly. We call this the “equation-free” approach, since it circumvents the step of obtaining accurate macroscopic descriptions. The backbone of this approach is the design of computational “experiments”. In traditional numerical analysis, the main code “pings“ a subroutine containing the model, and uses the returned information (time derivatives, etc.) to perform computer-assisted analysis. In our approach the same main code “pings“ a subroutine that runs an ensemble of appropriately initialized computational experiments from which the same quantities are estimated. Traditional continuum numerical algorithms can, thus, be viewed as protocols for experimental design (where “experiment“ means a computational experiment set up, and performed with a model at a different level of description). Ultimately, what makes it all possible is the ability to initialize computational experiments at will. Short bursts of appropriately initialized computational experimentation -through matrix-free numerical analysis, and systems theory tools like estimationbridge microscopic simulation with macroscopic modeling. If enough control authority exists to initialize laboratory experiments “at will” this computational enabling technology can lead to experimental protocols for the equation-free exploration of complex system dynamics.

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Citations
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Journal ArticleDOI

Network inoculation: Heteroclinics and phase transitions in an epidemic model.

TL;DR: A detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease is presented, showing that in this model, qualitative changes in the dynamics occur in two phase transitions.
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Detection of coarse-grained unstable states of microscopic/stochastic systems: a timestepper-based iterative protocol

TL;DR: In this paper, an iterative procedure based on the concept of coarse timestepping is proposed to detect coarse-grained hyperbolic unstable equilibria (saddle points) of microscopic simulators.
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Ergodic SDEs on submanifolds and related numerical sampling schemes

TL;DR: In this paper, the authors constructed a family of ergodic diffusion processes on the level set of a smooth function whose invariant measures coincide with the given one, and showed that the corresponding SDEs of these processes have relatively simple forms, and moreover, a consistent numerical scheme which samples the conditional measure asymptotically.
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MULTICUBED: Multiscale-multiphysics simulation of food processing

TL;DR: In this paper , the authors present a multiscale simulation framework for food structuring processes, which assumes models at three scales: the molecular microscale, the mesoscale of the food structure, and the macroscale of food products and processing equipment.
Journal ArticleDOI

Equation-free analysis of a dynamically evolving multigraph

TL;DR: The equation-free framework is used to analyze a dynamically evolving multigraph, based on coupling short intervals of direct dynamic network simulation with appropriately-defined lifting and restriction operators, which enables the acceleration of direct simulations through Coarse Projective Integration (CPI), as well as the identification of coarse stationary states via a Newton-GMRES method.
References
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Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI

Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes

TL;DR: In this paper, a numerical algorithm integrating the 3N Cartesian equations of motion of a system of N points subject to holonomic constraints is formulated, and the relations of constraint remain perfectly fulfilled at each step of the trajectory despite the approximate character of numerical integration.
Book

Iterative Methods for Sparse Linear Systems

Yousef Saad
TL;DR: This chapter discusses methods related to the normal equations of linear algebra, and some of the techniques used in this chapter were derived from previous chapters of this book.
Book

Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields

TL;DR: In this article, the authors introduce differential equations and dynamical systems, including hyperbolic sets, Sympolic Dynamics, and Strange Attractors, and global bifurcations.

A Reflection on Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields

TL;DR: In this paper, the authors introduce differential equations and dynamical systems, including hyperbolic sets, Sympolic Dynamics, and Strange Attractors, and global bifurcations.
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