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.

Another round of Catalyst Grants will be awarded in 2019. For more information, see http://micde.umich.edu/grants/catalyst-grants/

2018 Funded Projects


Exploring Quantum Embedding Methods for Quantum Computing

Researchers: Emanuel Gull, Physics; Dominika Zgid, Chemistry
Description:  The research team will design quantum embedding algorithms that can be early adopters of quantum computers on development of advanced materials for possible applications in modern batteries, next-generation oxide electronics, or high-temperature superconducting power cables.

Teaching autonomous soft machines to swim

Researchers:  Silas Alben, Mathematics; Robert Deegan, Physics, 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.

Caption: 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.

Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification

Researchers:  Valeriy Ivanov, Civil and Environmental Engineering; Nick Katopodes, Civil and Environmental Engineering; Khachik Sargsyan, Sandia National Labs
Description:  The research team will enhance urban flood monitoring and prediction using NASA Cyclone Global Navigation Satellite System (CYGNSS) data, taking advantage of state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

The problem of flood prediction incurs multiple uncertainties (a) and is of high computational complexity (b). New remote sensing data from the CYGNSS mission (c) can inform complex physically-based simulations (d) but in order to achieve feasibility of real-time solutions and uncertainty quantification, novel approaches are required. This project will develop reduced-order modeling tools (e) as innovative, parsimonious representation of rigorous hydrologic and hydrodynamic model formulations to efficiently obtain probability density distributions of one or many quantities of interest (f).

Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasets

Researcher: Omar Ahmed, Psychology, Neuroscience and Biomedical Engineering
Description: The team will develop a set of algorithms for use on high performance computers to analyze de-identified brain data from patients in order to better understand what electrical oscillations tell us about rapidly changing behavioral and pathological brain states.

Polar plots showing the rhythmic phases of spikes fired by human neurons, revealing systematic variations across space and time.

Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models

Researchers:  Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab
Description:  Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

Structure of a block-structured adaptive grid that overlays a so-called ‘cubed-sphere’ base grid. The atmospheric vortices (in color) resemble idealized tropical cyclones that are captured by the refined grid patches. The adaptations are guided by the location and strength of the rotational motion.

Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior

Researchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology
Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.

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

Deep Learning for Phylogenetic Inference

Researcher: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics
Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

Molecular sequence data are input into layers of neural network structures to produce a score for each possible tree topology describing how well the tree fits the data.

2017 Funded Projects


From Spiking Patterns to Memory formation — Tools for Analysis and Modeling of Network-wide Cognitive Dynamics of the Brain

Researchers: Sara Aton, Department of Molecular, Cellular and Developmental Biology and Michal Zochowski, Department of Physics, Biophysics Program
Description: The aim of the research is to develop models as well as analysis tools to understand network-wide spatio-temporal patterning underlying experimentally observed neural spiking activity. The research team has developed novel tools to analyze dynamics of neuronal representations across time, before during and after learning. These tools, for the first time, compare the stability of network dynamics before and after memory encoding.

Integral Equation Based Methods for Scientific Computing

Researcher: Robert Krasny, Department of Mathematics
Description: This project expands the application of numerical methods in which the differential equation is first converted into an integral equation by convolution with the Green’s function, followed by discretization and linear solution. Recent advances in numerical analysis and computing resources make this expansion possible, and the research team believes that integral equation-based numerical methods are superior to traditional methods in both serial and parallel computations. The project will attempt to apply these numerical methods to studies of viscous fluid flow, protein/solvent electrostatics, and electronic structure.

Computational Energy Systems

Researchers: Pascal Van Hentenryck, Industrial and Operations Engineering (IOE); E. Byon, IOE; R. Jiang, IOE; J. Lee, IOE; and J. Mathieu, Electrical Engineering and Computer Science
Description: The research team aims to develop new algorithms for the U.S. electrical power grid that integrate renewable energy sources, electrification of transportation systems, the increasing frequency of extreme weather events, and other emerging contingencies.

Black Swans, Dragon Kings, and the Science of Rare Events: Problems for the Exascale Era and Beyond

Researchers: Venkat Raman, Aerospace Engineering; Jacqueline Chen, Sandia National Laboratory; and Ramanan Sankaran, Oak Ridge National Laboratory.
Description: The purpose of the project is to develop the computational frameworks for exploring the tails of distributions, which lead to rare but consequential (and often catastrophic) outcomes. Two such rare events are “Black Swans” (occurring from pre-existing but unencountered events) and “Dragon Kings (occurring due to an external shock to the system). The methods developed are expected to have application in aerospace sciences, power generation and utilization, chemical processing, weather prediction, computational chemistry, and other fields.