David Flad

915 total citations · 1 hit paper
12 papers, 610 citations indexed

About

David Flad is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Atmospheric Science. According to data from OpenAlex, David Flad has authored 12 papers receiving a total of 610 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computational Mechanics, 6 papers in Statistical and Nonlinear Physics and 2 papers in Atmospheric Science. Recurrent topics in David Flad's work include Fluid Dynamics and Turbulent Flows (6 papers), Model Reduction and Neural Networks (6 papers) and Advanced Numerical Methods in Computational Mathematics (3 papers). David Flad is often cited by papers focused on Fluid Dynamics and Turbulent Flows (6 papers), Model Reduction and Neural Networks (6 papers) and Advanced Numerical Methods in Computational Mathematics (3 papers). David Flad collaborates with scholars based in Germany, United States and Russia. David Flad's co-authors include Andrea Beck, Claus‐Dieter Munz, Gregor J. Gassner, Florian Hindenlang, Matteo Parsani, Johan Larsson, Iván Bermejo-Moreno, Florian Menter, Daniel J. Garmann and Ali Uzun and has published in prestigious journals such as Journal of Computational Physics, International Journal for Numerical Methods in Fluids and Flow Turbulence and Combustion.

In The Last Decade

David Flad

12 papers receiving 588 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep neural networks for data-driven LES closure models

2019 216 citations Andrea Beck, David Flad et al. Journal of Computational Physics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Flad Germany	8	548	212	107	84	83	12	610
Espen Åkervik Sweden	9	668 1.2×	185 0.9×	200 1.9×	109 1.3×	53 0.6×	13	729
Taraneh Sayadi France	11	496 0.9×	135 0.6×	187 1.7×	115 1.4×	17 0.2×	30	558
Jürgen Seidel United States	14	627 1.1×	221 1.0×	441 4.1×	76 0.9×	21 0.3×	115	770
Meilin Yu United States	15	462 0.8×	60 0.3×	324 3.0×	92 1.1×	26 0.3×	61	664
Doru Caraeni United States	8	798 1.5×	77 0.4×	123 1.1×	54 0.6×	45 0.5×	23	844
Mattias Chevalier Sweden	8	364 0.7×	98 0.5×	134 1.3×	82 1.0×	66 0.8×	16	420
Samir Beneddine France	10	364 0.7×	67 0.3×	166 1.6×	57 0.7×	23 0.3×	17	526
Brian C. Vermeire Canada	15	528 1.0×	101 0.5×	161 1.5×	116 1.4×	109 1.3×	45	664
Jian Yu China	13	409 0.7×	170 0.8×	147 1.4×	32 0.4×	18 0.2×	54	573
Jonathan F. MacArt United States	11	288 0.5×	82 0.4×	74 0.7×	57 0.7×	28 0.3×	29	339

Countries citing papers authored by David Flad

Since Specialization

Citations

This map shows the geographic impact of David Flad's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Flad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Flad more than expected).

Fields of papers citing papers by David Flad

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Flad. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Flad. The network helps show where David Flad may publish in the future.

Co-authorship network of co-authors of David Flad

This figure shows the co-authorship network connecting the top 25 collaborators of David Flad. A scholar is included among the top collaborators of David Flad based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Flad. David Flad is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Garmann, Daniel J., Johan Larsson, Iván Bermejo-Moreno, et al.. (2024). Summary of the Wall-Modeled LES Test Suite From the 2024 AIAA High-Fidelity CFD Verification Workshop. 2 indexed citations

2.

Menter, Florian, et al.. (2023). Large Eddy Simulations for the Ahmed Car at 25° Slant Angle at Different Reynolds Numbers. Flow Turbulence and Combustion. 112(1). 321–343. 1 indexed citations

3.

Beck, Andrea, et al.. (2020). A neural network based shock detection and localization approach for discontinuous Galerkin methods. Journal of Computational Physics. 423. 109824–109824. 41 indexed citations

4.

Flad, David, et al.. (2020). A large eddy simulation method for DGSEM using non-linearly optimized relaxation filters. Journal of Computational Physics. 408. 109303–109303. 7 indexed citations

5.

Beck, Andrea, David Flad, & Claus‐Dieter Munz. (2019). Deep neural networks for data-driven LES closure models. Journal of Computational Physics. 398. 108910–108910. 216 indexed citations breakdown →

6.

Beck, Andrea, David Flad, & Claus‐Dieter Munz. (2018). Neural Networks for Data-Based Turbulence Models. arXiv (Cornell University). 3 indexed citations

7.

Beck, Andrea, David Flad, & Claus‐Dieter Munz. (2018). Deep Neural Networks for Data-Driven Turbulence Models. arXiv (Cornell University). 16 indexed citations

8.

Flad, David & Gregor J. Gassner. (2017). On the use of kinetic energy preserving DG-schemes for large eddy simulation. Journal of Computational Physics. 350. 782–795. 66 indexed citations

9.

Beck, Andrea, et al.. (2016). On the Influence of Polynomial De-aliasing on Subgrid Scale Models. Flow Turbulence and Combustion. 97(2). 475–511. 27 indexed citations

10.

Flad, David, Andrea Beck, & Claus‐Dieter Munz. (2015). Simulation of underresolved turbulent flows by adaptive filtering using the high order discontinuous Galerkin spectral element method. Journal of Computational Physics. 313. 1–12. 53 indexed citations

11.

Beck, Andrea, et al.. (2014). High‐order discontinuous Galerkin spectral element methods for transitional and turbulent flow simulations. International Journal for Numerical Methods in Fluids. 76(8). 522–548. 163 indexed citations

12.

Flad, David, Andrea Beck, Gregor J. Gassner, & Claus‐Dieter Munz. (2014). A Discontinuous Galerkin Spectral Element Method for the direct numerical simulation of aeroacoustics. 15 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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