Krishna Garikipati
Applications:
Biological Sciences, Complex Systems, Engineering, Physical Science
Methodologies:
Artificial Intelligence, Bayesian Methods, Computing, Graph-Based Methods, Machine Learning

Krishna Garikipati

Adjunct Professor

Mechanical Engineering, College of Engineering

Professor of Mechanical Engineering, College of Engineering and Professor of Mathematics, College of Literature, Science, and the Arts

My research is in computational science and scientific artificial intelligence, including machine learning and data-driven modelling. I have applied these approaches to physics discovery by model inference, scale bridging, partial differential equation solvers, representation of complexity and constructing reduced-order models of high-dimensional systems. My research is motivated by and applied to phenomena in bioengineering, biophysics, mathematical biology and materials physics. Of specific interest to me are patterning and morphogenesis in developmental biology, cellular biophysics, soft matter and mechano-chemical phase transformations in materials. More fundamentally, the foundations of my research lie in applied mathematics, numerical methods and scientific computing.

A schematic illustrating the range of ML methods comprising the mechanoChemML code framework for data-driven computational material physics.