Institute for Computational and Mathematical Engineering (ICME)
Showing 1-7 of 7 Results
Professor of Mechanical EngineeringOn Leave from 01/01/2022 To 03/31/2022
Current Research and Scholarly InterestsProfessor Darve's research is focused on the development of numerical methods for high-performance scientific computing, numerical linear algebra, fast algorithms, parallel computing, anomaly detection, and machine learning with applications in engineering.
Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioStefan's research interest rests within low-Mach turbulent fluid mechanics methods development for complex systems that drive the coupling of mass, momentum, species and energy transport. His core research resides within the intersection of physics model development, numerical methods research, V&V techniques exploration, and high performance computing and coding methods for low-Mach turbulent flow applications. Dr. Domino also supports the co-teaching of ME469 while continuing his primary career at Sandia National Laboratories.
University of Utah
Ph.D. Department of Chemical Engineering, 1999
"Methods towards improved simulations for the oxides of nitrogen in pulverized-coal furnaces"
Professor Philip J. Smith, Advisor
Select Recent Publications:
* Barone, M., Ray, J., Domino, S. P., "Feature selection, clustering, and prototype placement for turbulence datasets", AIAA Journal, 2021, https://doi.org/10.2514/1.J060919.
* Domino, S. P., Hewson, J., Knaus, R., Hansen, M., "Predicting large-scale pool fire dynamics using an unsteady flamelet- and large-eddy simulation-based model suite", Physics of Fluids, 2021, https://doi.org/10.1063/5.0060267 (Editor's pick: August 4, 2021).
* Domino, S. P., "A case study on pathogen transport, deposition, evaporation and transmission: linking high-fidelity computational fluid dynamics simulations to probability of infection", Int. J. CFD, 2021, https://doi.org/10.1080/10618562.2021.1905801.
* Domino, S. P., Pierce, F., Hubbard, J., "A multi-physics computational investigation of droplet pathogen transport emanating from synthetic coughs and breathing", Atom. Sprays, 2021, https://doi.org/10.1615/AtomizSpr.2021036313.
* Jofre, L., Domino, S. P., Iaacarino, G., "Eigensensitivity analysis of subgrid-scale stresses in large-eddy simulation of a turbulent axisymmetric jet", Int. J. Heat Mass, 2019, https://doi.org/DOI:10.1016/J.IJHEATFLUIDFLOW.2019.04.014.
* Domino, S. P., Sakievich, P., Barone, M., "An assessment of atypical mesh topologies for low-Mach large-eddy simulation", Comp. Fluids, 2019, https://doi.org/10.1016/j.compfluid.2018.12.002.
* Domino, S. P., "Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach ", J. Comput. Physics, 2018, https://doi.org/10.1016/j.jcp.2018.01.007.
* Jofre, L., Domino, S. P., Iaacarino, G., "A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures", Flow Turb. Combust., 2018, https://doi.org/10.1007/s10494-017-9844-8.
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
BioDavid Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.
My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems.
Associate Professor of Computer Science and, by courtesy, of Molecular and Cellular Physiology and of Structural Biology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
Associate Professor of Geophysics
Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.