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Journal ArticleDOI

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

Dynamic calibration of agent-based models using data assimilation

TL;DR: This paper describes how ABMs can be dynamically calibrated using the ensemble Kalman filter (EnKF), a standard method of data assimilation, and builds a hierarchy of exemplar models that are used to demonstrate how to apply the EnKF and calibrate these using open data of footfall counts in Leeds.
Journal ArticleDOI

Computational Approaches for the Dynamics of Structure Formation in Self-Assembling Polymeric Materials

TL;DR: In this paper, the authors discuss opportunities and challenges of recently developed computational strategies to predict the dynamics of self-assembly of polymeric materials on the basis of the underlying free-energy landscape.
Journal ArticleDOI

Towards a comprehensive framework for modeling urban spatial dynamics

TL;DR: This work proposes a comprehensive modeling approach that is comprised of bottom-up and top-down models in which both inductive and deductive approaches are used to describe and explain urban spatial dynamics.
Book ChapterDOI

Diffusion Maps - a Probabilistic Interpretation for Spectral Embedding and Clustering Algorithms

TL;DR: This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and cataloging individual neurons in the brain.
Journal ArticleDOI

Application of nonlinear dimensionality reduction to characterize the conformational landscape of small peptides.

TL;DR: The recently proposed ScIMAP algorithm is used to automatically extract motion parameters from simulation data and proves as excellent reaction coordinates for the molecules studied and can be used to identify stable states and transitions, and as a basis to build free‐energy surfaces.
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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