Long time-scale simulations using exponential time-propagators
Researchers: Vikram Gavini (Mechanical Engineering)
Description: This effort is aimed at developing scalable and efficient algorithms for long-time scale simulations of dynamical phenomena in materials. The approaches to be developed are expected to enhance our ability to study a wide range of time-dependent phenomena from electron dynamics to elastic response of materials.
Algorithmic solutions to manage power consumption on exascale systems
Researchers: Eric Johnsen (Mechanical Engineering), Henry Hoffman (University of Chicago), and Jeffrey Hittinger (Lawrence Livermore National Lab)
Description: Our objective is to develop a quantitative strategy for power management at the exascale, given a desired solution accuracy. For this purpose, our approach integrates high-order methods development with mixed-precision computing, lossy data compression, and applications monitoring power consumption.
Convergence rate of Recovery DG vs. BR2 (the current gold standard) for linear diffusion on a Cartesian mesh. For the same p=2, Recovery achieves 8th order for Recovery, while BR2 is 3rd order.
Simulation-based discovery of robust algorithms for targeting of infectious disease screening and intervention
Researcher: Jon Zelner (Epidemiology), and Seth Guikema (Industrial and Operations Engineering and Civil and Environmental Engineering)
Description: In settings with a high burden of infectious diseases, such as Tuberculosis (TB), there is a growing need for tools that can help public health professionals find and treat cases more quickly and effectively. In this project, we will be continuing development of a spatiotemporal simulation model of coupled household and community TB in a high-incidence setting. In our initial work on this project, we have used this model to compare different intervention scenarios that take advantage of information on the spatial location and contact networks of TB cases to target interventions. In the next phase of this project, we plan to use this simulation platform as a tool for developing adaptive interventions that can respond to changing epidemiological conditions, i.e. a sharp rise in incidence indicative of an outbreak, and modify screening and intervention strategies to deal with the types of heterogeneity that makes tackling real-world infectious disease problems highly challenging.
Spatial hotspot of strongly elevated multi-drug resistant (MDR-TB) incidence in Lima, Peru illustrated in red. (Figure from Zelner et. al., JID 2016 )
J. Havumaki ,T. Cohen, C. Zhai, J. C. Miller,S. D. Guikema, M. C. Eisenberg, J. Zelner. “Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns.” PLoS computational biology vol. 17, 2 e1008713. 8 Feb. 2021, doi:10.1371/journal.pcbi.1008713
Nina B. Masters, Marisa C. Eisenberg, Paul L. Delamater, Matthew Kay, Matthew L. Boulton, Jon Zelner. “Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data.” Proceedings of the National Academy of Sciences Nov 2020, 117 (45) 28506-28514; DOI: 10.1073/pnas.2011529117