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Faculty
kinderlehrer
David Kinderlehrer, Alumni Professor of Mathematical Sciences,
    Professor of Materials Science and Engineering
Ph.D., University of California, Berkeley
E-mail: davidk@andrew.cmu.edu
Office: Wean Hall 7208
Phone: 412-268-5729


Research:

Kinderlehrer's recent activity is in applied mathematics and in analysis, in particular partial differential equations. The work in applied mathematics, joint with Shlomo Ta'asan, colleagues in the Materials Science and Engineering Dept., and our extraordinary postdocs, is directed toward understanding evolution of material microstructure. Nearly all technologically useful materials are polycrystalline microstructures composed of a myriad of small crystallites or grains separated by grain boundaries, and comprise cellular networks. A central problem in materials is to develop technologies capable of producing an arrangement, or ordering, of the grains in terms of geometry and crystallographic texture that provides desired properties for a given function. The order, if indeed it is present at all, must be conferred by the network grain boundaries or interfaces, because they are what changes during the coarsening process. Using new experimental techniques and especially developed large scale simulation, we have discovered the grain boundary character distribution (GBCD), a statistic which details texture evolution. In the simplest situation, it is a Boltzmann distribution related to the interface energy density. Employing innovative methods in analysis, especially (Monge-Kantorovich) mass transport theory, we have, further, developed an entropy based theory that explains GBCD behavior. Thus materials arise with (non random) texture order, a new discovery. In this adventure, we have also found connections to other areas, for example, an analogue to prefix codes in information theory. The issue we face in this investigation is that stochastic or probabilistic behavior of the system is high, yet its quantitative resolution requires continuum scale methods that are very challenging to discover.

Additional work is in applications to cell biology, especially mechanisms of intracellular transport like protein motors, and models of ion transport, the Poisson-Nernst Planck Equations. These all share the features of stochastic behavior that requires continuum level upscaling for predictive modeling.


Recent CNA Publications: