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ORCIDHeinz Pitsch
Person information
- affiliation: Institute for Combustion Technology, RWTH Aachen, Aachen, Germany
- affiliation: Center for Turbulence Research, Stanford University, CA, USA
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Journal Articles
- 2023
[j16]Mathis Bode, Abhishek Deshmukh, Tobias Falkenstein, Seongwon Kang, Heinz Pitsch:
Hybrid scheme for complex flows on staggered grids and application to multiphase flows. J. Comput. Phys. 474: 108478 (2023)- 2022
- [j15]Fabian Fröde, Temistocle Grenga
, Vincent Le Chenadec, Mathis Bode, Heinz Pitsch:
A three-dimensional cell-based volume-of-fluid method for conservative simulations of primary atomization. J. Comput. Phys. 465: 111374 (2022) - 2016
- [j14]Philipp Trisjono, Seongwon Kang, Heinz Pitsch:
On a consistent high-order finite difference scheme with kinetic energy conservation for simulating turbulent reacting flows. J. Comput. Phys. 327: 612-628 (2016) - 2015
- [j13]Christoph Schmitt, Heinz Pitsch:
Reactive linearized equations of perturbed compressible variables for low-Mach number variable-density flows. J. Comput. Phys. 281: 1-27 (2015) - [j12]Seung Hyun Kim, Heinz Pitsch:
On the lattice Boltzmann method for multiphase flows with large density ratios. J. Comput. Phys. 303: 19-27 (2015) - 2013
- [j11]Vincent Le Chenadec, Heinz Pitsch:
Combination of 3D unsplit forward and backward volume-of-fluid transport and coupling to the level set method. J. Comput. Phys. 233: 10-33 (2013) - [j10]Vincent Le Chenadec, Heinz Pitsch:
A monotonicity preserving conservative sharp interface flow solver for high density ratio two-phase flows. J. Comput. Phys. 249: 185-203 (2013) - [j9]Seongwon Kang, Heinz Pitsch, Nahmkeon Hur:
On a robust ALE method with discrete primary and secondary conservation. J. Comput. Phys. 254: 1-7 (2013) - 2012
- [j8]Venkatasubramanian Viswanathan, Frank Wang, Heinz Pitsch:
Monte Carlo-Based Approach for Simulating Nanostructured Catalytic and Electrocatalytic Systems. Comput. Sci. Eng. 14(2): 60-69 (2012) - 2009
- [j7]Olivier Desjardins, Heinz Pitsch:
A spectrally refined interface approach for simulating multiphase flows. J. Comput. Phys. 228(5): 1658-1677 (2009) - 2008
- [j6]Olivier Desjardins, Guillaume Blanquart, Guillaume Balarac, Heinz Pitsch:
High order conservative finite difference scheme for variable density low Mach number turbulent flows. J. Comput. Phys. 227(15): 7125-7159 (2008) - [j5]Olivier Desjardins, Vincent Moureau, Heinz Pitsch:
An accurate conservative level set/ghost fluid method for simulating turbulent atomization. J. Comput. Phys. 227(18): 8395-8416 (2008) - [j4]Seung Hyun Kim, Heinz Pitsch, Iain D. Boyd:
Accuracy of higher-order lattice Boltzmann methods for microscale flows with finite Knudsen numbers. J. Comput. Phys. 227(19): 8655-8671 (2008) - [j3]Matthias Ihme, Alison L. Marsden, Heinz Pitsch:
Generation of Optimal Artificial Neural Networks Using a Pattern Search Algorithm: Application to Approximation of Chemical Systems. Neural Comput. 20(2): 573-601 (2008) - 2007
- [j2]Vincent Moureau, P. Minot, Heinz Pitsch, Claude Bérat:
A ghost-fluid method for large-eddy simulations of premixed combustion in complex geometries. J. Comput. Phys. 221(2): 600-614 (2007) - [j1]Vincent Moureau, Claude Bérat, Heinz Pitsch:
An efficient semi-implicit compressible solver for large-eddy simulations. J. Comput. Phys. 226(2): 1256-1270 (2007)
Conference and Workshop Papers
- 2020
- [c9]Giuseppe D'Alessio, Antonio Attili, Alberto Cuoci, Heinz Pitsch, Alessandro Parente:
Unsupervised Data Analysis of Direct Numerical Simulation of a Turbulent Flame via Local Principal Component Analysis and Procustes Analysis. SOCO 2020: 460-469 - 2019
- [c8]Dennis Ritter, Metin Korkmaz, Heinz Pitsch, Dirk Abel, Thivaharan Albin:
Optimization-based fuel injection rate digitalization for combustion rate shaping. ACC 2019: 5103-5110 - [c7]Mathis Bode, Michael Gauding, Konstantin Kleinheinz, Heinz Pitsch:
Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction. ISC Workshops 2019: 541-560 - 2018
- [c6]Mathis Bode, Michael Gauding, Jens Henrik Göbbert, Baohao Liao, Jenia Jitsev, Heinz Pitsch:
Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning. ISC Workshops 2018: 614-623 - 2016
- [c5]Mathis Bode, Marco Davidovic, Heinz Pitsch:
Multi-scale Coupling for Predictive Injector Simulations. JHPCS 2016: 96-108 - [c4]Jens Henrik Göbbert, Hristo Iliev, Cedrick Ansorge, Heinz Pitsch:
Overlapping of Communication and Computation in nb3dfft for 3D Fast Fourier Transformations. JHPCS 2016: 151-159 - 2015
- [c3]Jens Henrik Göbbert, Michael Gauding, Cedrick Ansorge, Bernd Hentschel, Torsten W. Kuhlen, Heinz Pitsch:
Direct Numerical Simulation of Fluid Turbulence at Extreme Scale with psOpen. PARCO 2015: 777-785 - 2013
- [c2]René Zweigel, Thiva Albin, Frank J. Hesseler, B. Jochim, Heinz Pitsch, Dirk Abel:
Greybox modeling of the diesel combustion by use of the scalar dissipation rate. ECC 2013: 3961-3966 - 2005
- [c1]Rob J. M. Bastiaans, S. M. Martin, Heinz Pitsch, Jeroen A. van Oijen, L. Philip H. de Goey:
Flamelet Analysis of Turbulent Combustion. International Conference on Computational Science (3) 2005: 64-71
Informal and Other Publications
- 2022
- [i4]Mathis Bode, Michael Gauding, Jens Henrik Göbbert, Baohao Liao, Jenia Jitsev, Heinz Pitsch:
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning. CoRR abs/2210.16110 (2022) - [i3]Mathis Bode, Michael Gauding, Dominik Goeb, Tobias Falkenstein, Heinz Pitsch:
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data. CoRR abs/2210.16206 (2022) - 2019
- [i2]Mathis Bode, Michael Gauding, Konstantin Kleinheinz, Heinz Pitsch:
Deep learning at scale for subgrid modeling in turbulent flows. CoRR abs/1910.00928 (2019) - [i1]Mathis Bode, Michael Gauding, Zeyu Lian, Dominik Denker, Marco Davidovic, Konstantin Kleinheinz, Jenia Jitsev, Heinz Pitsch:
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows. CoRR abs/1911.11380 (2019)
Coauthor Index
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