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20212026
most citedApplying Bayesian Optimization with Gaussian Process Regression to Computational Fluid Dynamics Problems

9 citations · 11 across the 4 of their papers we have counts for

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physics.flu-dyn2026★ 1 cited

Causally coherent structures in turbulent dynamical systems

Daniele Massaro, Saleh Rezaeiravesh, Philipp Schlatter

The extraction of spatio-temporal coherence in high-dimensional, chaotic, non-linear dynamical systems, such as turbulent flows, remains a fundamental challenge in physics, mathema…

physics.flu-dyn2025

Effects of lower floating-point precision on scale-resolving numerical simulations of turbulence

Martin Karp, Ronith Stanly, Timofey Mukha +11

Modern computing clusters offer specialized hardware for reduced-precision arithmetic that can speed up the time to solution significantly. This is possible due to a decrease in da…

physics.flu-dyn2025

Robustness and uncertainty of direct numerical simulation under the influence of rounding and noise

Martin Karp, Niclas Jansson, Saleh Rezaeiravesh +2

Numerical precision in large-scale scientific computations has become an emerging topic due to recent developments in computer hardware. Lower floating point precision offers the p…

physics.flu-dyn2023★ 1 cited

In-situ Estimation of Time-averaging Uncertainties in Turbulent Flow Simulations

Saleh Rezaeiravesh, Christian Gscheidle, Adam Peplinski +2

The statistics obtained from turbulent flow simulations are generally uncertain due to finite time averaging. The techniques available in the literature to accurately estimate thes…

physics.flu-dyn2021★ 9 cited

Applying Bayesian Optimization with Gaussian Process Regression to Computational Fluid Dynamics Problems

Y. Morita, S. Rezaeiravesh, N. Tabatabaei +3

Bayesian optimization (BO) based on Gaussian process regression (GPR) is applied to different CFD (computational fluid dynamics) problems which can be of practical relevance. The p…