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A framework for multi-scale modelling.

B Chopard1, Joris Borgdorff2, A G Hoekstra3

  • 1Department of Computer Science, University of Geneva, Geneva, Switzerland bastien.chopard@unige.ch.

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Summary
This summary is machine-generated.

This study presents a framework for designing and executing complex multi-scale and multi-science numerical simulations by coupling single-scale submodels. The methodology enables a unified approach to computational modeling across diverse scientific fields.

Keywords:
frameworkmethodologymodellingmulti-scalemulti-scale parallelismsimulation

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Area of Science:

  • Computational science
  • Numerical simulations
  • Scientific modeling

Background:

  • Designing and executing multi-scale and multi-science numerical simulations presents significant challenges.
  • Existing methodologies may lack a unified framework for integrating diverse scientific models.
  • The complexity of these simulations requires robust and adaptable computational approaches.

Purpose of the Study:

  • To review and define a methodology for designing, implementing, and executing multi-scale and multi-science numerical simulations.
  • To identify and precisely define the key components of multi-scale modeling.
  • To establish a framework that bridges application design and computer implementation for complex simulations.

Main Methods:

  • Formulating multi-scale models as collections of coupled single-scale submodels.
  • Utilizing concepts like the scale separation map and the generic submodel execution loop (SEL).
  • Employing coupling templates to define a multi-scale modeling language.

Main Results:

  • A comprehensive framework for multi-scale and multi-science numerical simulations has been reviewed and defined.
  • Key ingredients of multi-scale modeling are identified and precisely defined.
  • The proposed methodology facilitates the integration of different scientific domains within a single simulation framework.

Conclusions:

  • The presented methodology provides a structured approach to developing complex numerical simulations.
  • The framework has demonstrated successful application across various scientific and technological fields.
  • This approach enhances the design and implementation of multi-scale and multi-science computational models.