Institute for Computational and Mathematical Engineering (ICME)
Showing 21-40 of 163 Results
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Haoxuan Chen
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2022
BioPersonal website: https://haoxuanstevec00.github.io/
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Marta D'Elia
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioI’m a research/computational scientist working on the design and analysis of models and data-driven algorithms for the simulation of complex, multiscale and multiphysics problems. My background and training have foundations in Numerical Analysis, Scientific Computing, Inverse Problems, Control and Optimization, and Uncertainty Quantification. In the past five years I have focused on Scientific Machine Learning (SciML) and Deep Learning. I am an expert in Nonlocal/Fractional Modeling and Simulation (10 years) with application to Continuum Mechanics, Subsurface Transport, Image Processing, and Turbulence. I have a master's degree in Mathematical Engineering from Politecnico di Milano (2007) and a PhD in Applied Mathematics from Emory University (2011).
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Eric Darve
Director, Institute for Computational and Mathematical Engineering (ICME) and Professor of Mechanical Engineering
Current Research and Scholarly InterestsThe research interests of Professor Darve span across several domains, including machine learning for science and engineering, large-language models, transformer models, surrogate and reduced order modeling, stochastic inversing, anomaly detection, numerical linear algebra, high-performance, parallel, and GPU computing.
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Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioDr. Domino’s research interest rests within low-Mach 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 elucidation, numerical methods research, V&V techniques exploration, and high performance computing and coding methods for turbulent flow applications. Stefan also supports the teaching of ME469, Computational Methods in Fluid Mechanics, while continuing his primary career at Sandia National Laboratories as a Distinguished Member of the Technical Staff.
Education:
University of Utah
Ph.D. Department of Chemical Engineering, 2000
"Methods towards improved simulations for the oxides of nitrogen in pulverized-coal furnaces"
Professor Philip J. Smith, Advisor
Select Recent Publications:
* Domino, S. P., Scott, S., Hubbard, J., "Structural uncertainty assessment for fire-engulfed objects in crosswind: Establishing credibility for a multiphysics wall-modeled large-eddy simulation paradigm", Phys. Rev. Fluids, 2025. (PR Journal Club, May 1, 2025)
* Domino, S. P., "On the subject of large-scale pool fires and turbulent boundary layer interactions", Phys. Fluids, 2024. (Featured)
* Domino, S. P., Wenzel, E. A, "A direct numerical simulation study for confined non-isothermal jet impingement at moderate nozzle-to-plate distances: capturing jet-to-ambient density effects", Int. J. Heat Mass Trans, 2023.
* Benjamin, M., Domino, S. P., Iaccarino, G., "Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges", Eur. Phys. J. E., 2023.
* Hubbard, J., Cheng, M., Domino, S. P., "Mixing in low-Reynolds number reacting impinging jets in crossflow", J. Fluids Engr., 2023.
* Domino, S. P. “Unstructured finite volume approaches for turbulence,” in Numerical Methods in Turbulence Simulation, edited by R. Moser (Elsevier, 2023), Ch. 7, pp. 285–317.
* Scott, S., Domino, S. P., "A computational examination of large-scale pool fires: variations in crosswind velocity and pool shape", Flow, 2022.
* Domino, S. P., Horne, W., "Development and deployment of a credible unstructured, six-DOF, implicit low-Mach overset simulation tool for wave energy applications", Renew. Energy, 2022.
* Hubbard, J., Hansen, M., Kirsch, J., Hewson, J., Domino, S. P., “Medium scale methanol pool fire model validation”, J. Heat Transfer, 2022.
* Barone, M., Ray, J., Domino, S. P., "Feature selection, clustering, and prototype placement for turbulence datasets", AIAA J., 2021,
* 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", Phys. Fluids, 2021. (Editor's pick)
* 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.
* 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.
* 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 Fluid Flow, 2019.
* Domino, S. P., Sakievich, P., Barone, M., "An assessment of atypical mesh topologies for low-Mach large-eddy simulation", Comp. Fluids, 2019.
* Domino, S. P., "Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach ", J. Comput. Physics, 2018.
* Jofre, L., Domino, S. P., Iaacarino, G., "A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures", Flow Turb. Combust., 2018.
CV: https://github.com/spdomin/Present/blob/master/cv/dominoCV.pdf -
David Donoho
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.
Research Statement:
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. -
Ron Dror
Cheriton Family Professor and Professor, by courtesy, of Structural Biology and of Molecular & Cellular Physiology
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.
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Eric Dunham
Professor of Geophysics
On Leave from 01/01/2025 To 06/30/2025Current Research and Scholarly InterestsPhysics of natural hazards, specifically earthquakes, tsunamis, and volcanoes. Computational geophysics.
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Charbel Farhat
Vivian Church Hoff Professor of Aircraft Structures and Professor of Aeronautics and Astronautics
Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design, analysis, and digital twinning of complex systems in aerospace, marine, mechanical, and naval engineering. They contribute major advances to Simulation-Based Engineering Science. Current engineering foci in research are on reliable autonomous carrier landing in rough seas; dissipation of vertical landing energies through structural flexibility; nonlinear aeroelasticity of N+3 aircraft with High Aspect Ratio (HAR) wings; pulsation and flutter of a parachute; pendulum motion in main parachute clusters; coupled fluid-structure interaction (FSI) in supersonic inflatable aerodynamic decelerators for Mars landing; flight dynamics of hypersonic systems and their trajectories; and advanced digital twinning. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-component, multi-physics problems; discrete-event-free embedded boundary methods for CFD and FSI; efficient Bayesian optimization using physics-based surrogate models; modeling and quantifying model-form uncertainty; probabilistic, physics-based machine learning; mechanics-informed artificial neural networks for data-driven constitutive modeling; and efficient nonlinear projection-based model order reduction for time-critical applications such as design, active control, and digital twinning.
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Ron Fedkiw
Canon Professor in the School of Engineering
BioFedkiw's research is focused on the design of new computational algorithms for a variety of applications including computational fluid dynamics, computer graphics, and biomechanics.
