"The Michigan Institute for Computational Discovery and Engineering awarded its second round of Catalyst Grants in May 2018 to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success, potential for external funding, and potential to leverage ARC’s existing computing resources." 
             -The Michigan Institute for Computational Discovery & Engineering

Seven innovative projects in computational science will each receive between $80,000 and $90,000 for their research.

Chemical waves and elastic deformations in thin gel sheets. (a & b) Target and spiral waves in the BZ (Belousov-Zhabotinsky) chemical reaction. (c) Numerical simulations of the BZ reaction. (d) Resulting reference metrics and shapes for the gel sheet.(e) Experimental images of oscillating gels.

Teaching autonomous soft machines to swim

Researchers:  Silas Alben, Mathematics; Robert Deegan, Physics, Complex Systems, Alex Gorodetsky, Aerospace Engineering
Description:  Self-oscillating gels are polymeric materials that change shape, driven by chemical reactions occurring entirely within the gel. The research team will develop a computational and machine learning program to discover how to configure self-oscillating gels so that they undergo deformations that result in swimming. The long term goal is to develop a general framework for controlling autonomous soft machines.

Embedded sensing and machine learning to distinguish pollinators (Bombus impatiens pictured) from other sound sources in natural environments.

Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior

Researchers: Robert Dick, Electrical
Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary
Biology, Complex Systems; Paul Glaum, Ecology and Evolutionary

Description: To understand the
mechanisms driving the population dynamics of pollinators, the research team
will develop technologies for deeply embedded hardware/software learning