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1
Assessment of supervised machine learning methods for fluid flows
Assessment of supervised machine learning methods for fluid flows
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Assessment of supervised machine learning methods for fluid flows

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.497-519 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00518-y

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2
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
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Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.367-383 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00528-w

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3
De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets
De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets
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De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets

Theoretical and computational fluid dynamics, 2017-08, Vol.31 (4), p.349-368 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2017 ;COPYRIGHT 2017 Springer ;Theoretical and Computational Fluid Dynamics is a copyright of Springer, 2017. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-017-0432-2

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4
Correction to: Theoretical treatment of fluid flow for accelerating bodies
Correction to: Theoretical treatment of fluid flow for accelerating bodies
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Correction to: Theoretical treatment of fluid flow for accelerating bodies

Theoretical and computational fluid dynamics, 2023-02, Vol.37 (1), p.129-129 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2023 ;COPYRIGHT 2023 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2023. ;ISSN: 0935-4964 ;ISSN: 1432-2250 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-023-00639-0

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5
Super-resolution analysis via machine learning: a survey for fluid flows
Super-resolution analysis via machine learning: a survey for fluid flows
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Super-resolution analysis via machine learning: a survey for fluid flows

Theoretical and computational fluid dynamics, 2023-08, Vol.37 (4), p.421-444 [Peer Reviewed Journal]

The Author(s) 2023 ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-023-00663-0

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6
Recent improvements in the Zonal Detached Eddy Simulation (ZDES) formulation
Recent improvements in the Zonal Detached Eddy Simulation (ZDES) formulation
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Recent improvements in the Zonal Detached Eddy Simulation (ZDES) formulation

Theoretical and computational fluid dynamics, 2012-12, Vol.26 (6), p.523-550 [Peer Reviewed Journal]

Springer-Verlag 2011 ;COPYRIGHT 2012 Springer ;Springer-Verlag Berlin Heidelberg 2012 ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-011-0240-z

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7
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization
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Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization

Theoretical and computational fluid dynamics, 2021-10, Vol.35 (5), p.633-658 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 ;COPYRIGHT 2021 Springer ;The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-021-00580-0

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8
Special issue on machine learning and data-driven methods in fluid dynamics
Special issue on machine learning and data-driven methods in fluid dynamics
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Special issue on machine learning and data-driven methods in fluid dynamics

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.333-337 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00542-y

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9
Correction to: Unsteady lifting-line theory and the influence of wake vorticity on aerodynamic loads
Correction to: Unsteady lifting-line theory and the influence of wake vorticity on aerodynamic loads
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Correction to: Unsteady lifting-line theory and the influence of wake vorticity on aerodynamic loads

Theoretical and computational fluid dynamics, 2021-10, Vol.35 (5), p.757-758 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2021 ;COPYRIGHT 2021 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2021. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-021-00585-9

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10
Applications of the dynamic mode decomposition
Applications of the dynamic mode decomposition
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Applications of the dynamic mode decomposition

Theoretical and computational fluid dynamics, 2011-06, Vol.25 (1-4), p.249-259 [Peer Reviewed Journal]

Springer-Verlag 2010 ;Springer-Verlag 2011 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-010-0203-9

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11
Stability and modal analysis of shock/boundary layer interactions
Stability and modal analysis of shock/boundary layer interactions
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Stability and modal analysis of shock/boundary layer interactions

Theoretical and computational fluid dynamics, 2017-02, Vol.31 (1), p.33-50 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2016 ;COPYRIGHT 2017 Springer ;Theoretical and Computational Fluid Dynamics is a copyright of Springer, 2017. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-016-0397-6

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12
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
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A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.429-455 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-019-00512-z

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13
Deep model predictive flow control with limited sensor data and online learning
Deep model predictive flow control with limited sensor data and online learning
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Deep model predictive flow control with limited sensor data and online learning

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.577-591 [Peer Reviewed Journal]

The Author(s) 2020 ;COPYRIGHT 2020 Springer ;The Author(s) 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00520-4

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14
Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation
Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation
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Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.401-428 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00538-8

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15
Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks
Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks
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Machine learning for physics-informed generation of dispersed multiphase flow using generative adversarial networks

Theoretical and computational fluid dynamics, 2021-12, Vol.35 (6), p.807-830 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 ;COPYRIGHT 2021 Springer ;The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-021-00593-9

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16
Data-driven modeling of the chaotic thermal convection in an annular thermosyphon
Data-driven modeling of the chaotic thermal convection in an annular thermosyphon
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Data-driven modeling of the chaotic thermal convection in an annular thermosyphon

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.339-365 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00536-w

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17
Enhancement of shock-capturing methods via machine learning
Enhancement of shock-capturing methods via machine learning
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Enhancement of shock-capturing methods via machine learning

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.483-496 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00531-1

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18
On the accuracy of high-order discretizations for underresolved turbulence simulations
On the accuracy of high-order discretizations for underresolved turbulence simulations
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On the accuracy of high-order discretizations for underresolved turbulence simulations

Theoretical and computational fluid dynamics, 2013-06, Vol.27 (3-4), p.221-237 [Peer Reviewed Journal]

Springer-Verlag 2012 ;COPYRIGHT 2013 Springer ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-011-0253-7

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19
Dimensionality reduction and reduced-order modeling for traveling wave physics
Dimensionality reduction and reduced-order modeling for traveling wave physics
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Dimensionality reduction and reduced-order modeling for traveling wave physics

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.385-400 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-020-00529-9

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20
Low-order phenomenological modeling of leading-edge vortex formation
Low-order phenomenological modeling of leading-edge vortex formation
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Low-order phenomenological modeling of leading-edge vortex formation

Theoretical and computational fluid dynamics, 2013-09, Vol.27 (5), p.577-598 [Peer Reviewed Journal]

Springer-Verlag 2012 ;COPYRIGHT 2013 Springer ;Springer-Verlag Berlin Heidelberg 2013 ;ISSN: 0935-4964 ;EISSN: 1432-2250 ;DOI: 10.1007/s00162-012-0279-5

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