Optimal bio-locomotion strategies in fluids

Mattia Gazzola, University of Illinois at Urbana-Champaign

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Mattia Gazzola, Hang Yu, Tejaswin Parthasarathy, Xiaotian Zhang, Fan Kiat Chan, Vedant Puri, Nikhil Oberoi, Vedant Dubey, Dongxin Zhu, YASHRAJ BHOSALE, Gregory Stroot, Nikhil Anto Valluvan, Rushi Notaria

The curiosity towards biolocomotion problems has driven my research at the interface between minimal theoretical modeling, large scale computational physics and artificial intelligence. My work has been primarily concerned with the characterization of aquatic propulsion from an optimality standpoint, via inverse design. In a broad sense, the objective of an inverse design method is to find, in the vast space of possible design solutions, the best ones relative to a desired goal. A general purpose reverse engineering approach relies on an optimizer that drives the search and on numerical simulations to test candidate solutions. Simulations are the tool of choice since they are economically cheap, fast to develop, tremendously flexible and inherently suitable to be embedded in an automated design cycle. Once the optimal designs are identified, they are theoretically and experimentally analyzed to unveil their operating principles.

In this light, my long term ambition is to pursue a seamless integration of theory, high performance computing, and experiments in a reverse engineering cycle for the characterization of biophysical phenomena as well as for industrial and architectural design. In the context of rational design, I am particularly interested in exploring the use of underactuated systems in which centralized control is partially or completely outsourced to physics, by taking advantage of mechanical instabilities arising from body-environment interactions. Applications range from soft robotics, propulsion and energy harvesting to biomedical devices.