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Updated: Nov 4, 2025

Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry
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Mesoscopic and multiscale modelling in materials.

Jacob Fish1, Gregory J Wagner2, Sinan Keten2

  • 1Columbia University, New York, NY, USA. fishj@columbia.edu.

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This summary is machine-generated.

Multiscale modeling simulates large-scale behavior using fine-scale computational data, avoiding empirical models. This approach integrates diverse methods, including recent machine learning and material design techniques.

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Area of Science:

  • Computational Science
  • Materials Science
  • Mechanical Engineering

Background:

  • Multiscale modeling simulates continuum-scale behavior using finer-scale computational data.
  • It offers an alternative to traditional empirical constitutive models.
  • Numerous methods have been developed to bridge different length and time scales.

Purpose of the Study:

  • To introduce key concepts of multiscale modeling.
  • To present a diverse sampling of multiscale modeling methods.
  • To highlight recent advancements integrating machine learning and material design.

Main Methods:

  • Review and categorization of existing multiscale modeling techniques.
  • Discussion of approaches for bridging multiple length and time scales.
  • Inclusion of novel methods incorporating machine learning and material design.

Main Results:

  • A conceptual framework for understanding multiscale modeling.
  • A categorized overview of various multiscale simulation techniques.
  • Demonstration of the integration of advanced computational tools.

Conclusions:

  • Multiscale modeling is a powerful approach for simulating complex systems.
  • The field is rapidly evolving with new integrations like machine learning.
  • These methods enhance predictive capabilities in science and engineering.