Tülin Kaman
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Associate Professor
Also affiliated: University of Zurich (2017–2025); Purdue University West Lafayette (2023); ETH Zurich (2025); Collegium Helveticum (2025); Istanbul Technical University (2004); Stony Brook University (2011–2013)
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Tülin Kaman's research focuses on numerical simulations of fluid dynamics, particularly instabilities in fluid interfaces. She has investigated the Richtmyer-Meshkov Instability, exploring its statistical learning for nonlinear dynamical systems and its application to aircraft-UAV collisions. Her work includes the validation and verification of turbulence mixing associated with this instability, specifically for an air/SF$_6$ interface.
Kaman has also contributed to computational science education, developing an undergraduate course to train future computational scientists. Her research on parallel computing for turbulent mixing simulations highlights her interest in the performance analysis of computational fluid dynamics (CFD) codes. She has received federal funding from the National Science Foundation (NSF) for high-performance computing systems and for a spring lecture series in computational and applied mathematics. Kaman collaborates with researchers at the University of Arkansas at Fayetteville, including Ryan Holley, John McGarigal, Alaina Edwards, and Shannon Dingman.
Metrics
- h-index: 6
- Publications: 23
- Citations: 139
Positions
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Guest Professor 2024–presentUniversity of Zurich Department of Informatics ORCID
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Associate Professor 2023–presentUniversity of Arkansas at Fayetteville Mathematical Sciences ORCID
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Senior Fellow 2024–2025Collegium Helveticum Swiss Institute for Advanced Study ORCID
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Assistant Professor 2017–2023University of Arkansas Department of Mathematical Sciences ORCID
Selected Publications
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Simulations of two-dimensional single-mode Rayleigh-Taylor Instability using front-tracking/ghost-fluid method: comparison to experiments and theory (2025)arXiv (Cornell University) OpenAlex
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A front-tracking/ghost-fluid method for the numerical simulations of Richtmyer–Meshkov Instability (2025)
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Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions (2023)
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Performance Analysis of the Parallel CFD Code for Turbulent Mixing Simulations (2021)
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A crisis for the verification and validation of turbulence simulations (2020)
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A crisis for the V&V of turbulence simulations (2019)arXiv (Cornell University) OpenAlex
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V&V for turbulent mixing in the intermediate asymptotic regime (2018)
Federal Grants 2 $524,482 total
Spring Lecture Series in Computational and Applied Mathematics
Collaboration Network
Top Collaborators
- A crisis for the verification and validation of turbulence simulations
- V&V for turbulent mixing in the intermediate asymptotic regime
- A crisis for the V&V of turbulence simulations
- A crisis for the verification and validation of turbulence simulations
- V&V for turbulent mixing in the intermediate asymptotic regime
- A crisis for the V&V of turbulence simulations
- A crisis for the verification and validation of turbulence simulations
- V&V for turbulent mixing in the intermediate asymptotic regime
- A crisis for the V&V of turbulence simulations
- Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions
- Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions
- Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions
- Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions
- V&V for turbulent mixing in the intermediate asymptotic regime
- V&V for turbulent mixing in the intermediate asymptotic regime
- Performance Analysis of the Parallel CFD Code for Turbulent Mixing Simulations
- Performance Analysis of the Parallel CFD Code for Turbulent Mixing Simulations
- Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions
- Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions
- A front-tracking/ghost-fluid method for the numerical simulations of Richtmyer–Meshkov Instability
- Simulations of two-dimensional single-mode Rayleigh-Taylor Instability using front-tracking/ghost-fluid method: comparison to experiments and theory
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