407 Multiscale Modeling and Machine Learning in Biomechanics

  • Yaling Liu, Lehigh University
  • Lucy Zhang, Rensselaer Polytechnic Institute
  • Ying Li, University of Connecticut
  • Jianxun Wang, University of Notre Dame

The biological systems involve complex mechanics at different scales, from organs, tissues to molecules. Multiscale modeling of biological system across spatial and temporal scales are essential for fundamental biomechanics and applications. Meanwhile, machine learning has been merged as an additional tool for numerous biomechanical problems that cannot be handled by traditional physics-based modeling approach. Such data driven approach can be used to explore complex multiscale and multiphysics in biomechanics. The overarching goal of this mini-symposium is to bring together researchers with a variety of backgrounds to exchange ideas to address grand challenges in biomechanics using multiscale modeling and machine learning approaches. Some topics under this mini-symposium include, but are not limited to:

- Cellular and subcellular mechanics: Cell mechanics, adhesion, migration, cell-cell interaction, membrane mechanics, cytoskeleton and cell-ECM interaction, mechanotransduction in cells, cell organization, cellular uptake of nanoparticles etc.

- Molecular mechanics: Biomolecules such as proteins and DNAs, mechanosensing and mechanotransduction, virus and nanoparticles, etc.

- Biofluid and biotransport mechanics: Transport of biofluids such as blood flow, saliva, lymph flow; transport of cell, biomolecules and fluids etc.

- Biomechanics in diseases and disorders: Bio-tissue modeling of Lung diseases, cardiovascular diseases, and voice disorders, etc.

- Machine learning in biomechanics: machine learning in fluid dynamics, biomechanics, and image-based machine learning of biofluids, tissues and cells, etc.

- Data assimilation and inverse modeling in biomechanics: Variational or Bayesian data assimilation for data-model fusion and inverse problems in biofluids, tissues, and cells, etc.

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