Computational simulation of multiscale, multiphysics systems. Data-driven constitutive relations for hyperelastic materials. Nonlinear mode reduction. Information geometry for reduced stochastic models.
Professional backgroundUniversity of Arizona, Tucson, AZ, 1986-89, University of Arizona, Tucson, MS in Applied Mathematics, 1988
Development of numerical simulation tools to predict large-scale collective response of systems that are only known through interaction of microscopic components. Examples include: plastic deformation of metals from knowledge of lattice defect dynamics, microtubule mechanics from coarse-grained molecular dynamics, brittle fracture from accumulation of microcrack damage, protein folding from atomic-level dynamics, propagation of waves in biological media characterized at the cellular level.
Mathematical techniques include adaptive computation, mesh, algorithm, model, machine learning applied to prediction of constitutive laws, use of information geometry to extract stochastic process characterizations from data, nonlinear mode reduction based upon deep neural nets.
Data‐Driven Reduced‐Order Model of Microtubule Mechanics
Y. Feng, S. Mitran,
Cytoskeleton, 75, 2, 45-60, 2018
A Numerical Model of Cellular Blebbing: A Volume-Conserving, Fluid–Structure interaction Model of the Entire Cell
J. Young, S. Mitran,
Journal of Biomechanics, 43, 2, 210-220, 2010
Metachronal Wave formation in A Model of Pulmonary Cilia
Computers & Structures, 85,11-14, 763-774, 2007