About

Hi there! My name is Andre Weiner. I am a research scientist working on computational fluid dynamics (CFD) and machine learning (ML). Besides ML and CFD, I am also interested in making research reproducible by employing containerization and version control (Docker, Singularity/Apptainer, Git, Github). Reproducibility also implies full transparency regarding source code and workflows, which is why my work as well as the work of students I supervise is available on Github. I am also an advocate for self-determined, lifelong learning.

That’s me in 2017.

Profile pages

If you are interested in my academic curriculum, have a look at the following profile pages:

• Google Scholar (journal articles)
• Technical University of Dresden (since Sep 2023)
• Technical University of Braunschweig (May 2020 - Jul 2023)
• Technical University of Darmstadt (Oct 2014 - Apr 2020)

Scientific software packages

• flowTorch - a Python library for analysis and reduced-order modeling of fluid flows, Github
• drlFoam - Deep Reinforcement Learning with OpenFOAM, Github

University courses

• Machine learning in computational fluid dynamics, TU Braunschweig and TU Dresden, since winter term 2021/2022; I designed this course from scratch; lecture notes, slides, exercises, and datasets are freely available
• Modeling and simulation of turbulent flows, TU Braunschweig, summer term 2023; I designed this course mostly from scratch (syllabus, lecture slides, exercises)
• Computational fluid dynamics, TU Dresden, since winter term 2024/2025

Presentation and training slides

• Habilitationsvorhaben, faculty council mechanical engineering, Oct 2025, Dresden
• Bayesian parameter and design optimization, 20th OpenFOAM workshop, Jul 2025, Vienna
• Analyse turbulenter Strömungen - Wie man Ordnung im Chaos findet, Lange Nacht der Wissenschaften, Jun 2025, Dresden
• Model-based DRL for accelerated learning from flow simulations, seminar talk ILR TU Berlin, Feb 2025, Berlin
• Data-driven modeling, optimization, and control in CFD, invited talk automotive industry, Feb 2025
• A brief introduction to Bayesian optimization, PSM seminar, Oct 2024, Dresden
• Best practice guidelines for the modal analysis of numerical and experimental data, DLRK 2024, Sep 2024, Hamburg
• An optimized dynamic mode decomposition for flow analysis and forecasting, 19th OpenFOAM workshop, June 2024, Beijing
• Machine learning in computational fluid dynamics - an overview, invited talk, automotive industry, July 2024
• Combining machine learning with computational fluid dynamics using OpenFOAM and SmartSim, 19th OpenFOAM workshop, June 2024, Beijing
• Analyse turbulenter Strömungen - wie man Ordnung im Chaos findet, Lange Nacht der Wissenschaft, June 2024, Dresden
• Modal analysis of fluid flows - best practices, ISM seminar - invited talk, May 2024, Braunschweig
• Model-based reinforcement learning for active flow control, 94th GAMM, Mar 2024, Magdeburg
• Extracting coherent structures and reduced-order models from flows, Bosch - invited talk, Jan 2024, Stuttgart
• DMD analysis of the XRF-1 wing undergoing transonic shock buffet, DLRK 2023, Sep 2023, Stuttgart
• Advances in the application of DRL to flow control, 18th OpenFOAM workshop, Jul 2023, Genoa
• Setting up a new flow control problem in drlFoam, 18th OpenFOAM workshop, Jul 2023, Genoa
• Model-based deep reinforcement learning for flow control, 22nd Computer Fluids Conference, Apr 2023, Cannes
• Data-driven modeling and validation of reactive mass transfer at rising bubbles, OpenFoam multiphase short course - invited talk, Mar 2023, Darmstadt
• ProDiGI - Machine learning in computational fluid dynamics lecture, ProDiGI event, Feb 2023, Braunschweig
• Analysis of shock buffet PSP measurements by means of dynamic mode decomposition, Airbus XRF-1 workshop, Dec 2022, virtual
• Activities of the data-driven modeling special interest group, OpenFOAM-v2206 release webinar, Jul 2022, virtual
• Modal analysis of transonic shock buffets on a NACA-0012 airfoil, 17th OpenFOAM workshop, Jul 2022, Cambridge
• Analyzing coherent structures by means of dynamic mode decomposition, 17th OpenFOAM workshop, Jul 2022, Cambridge
• Active flow control via deep reinforcement learning implemented in OpenFOAM, OpenFOAM combustion seminar - invited talk, May 2022, Singapore, virtual
• Simulation and modal analysis of transonic shock buffets on a NACA-0012 airfoil, Euromech Colloquium 612, Mar 2022, Aachen, virtual
• Machine learning in computational fluid dynamics - an overview, keynote lecture chemical industry, Feb 2022
• Simulation and modal analysis of transonic shock buffets on a NACA-0012 airfoil, AIAA SciTech Forum, Jan 2022, San Diego, virtual
• Active control of the flow past a cylinder using deep reinforcement learning, OpenFOAM conference, Oct 2021, virtual
• flowTorch - A platform for analysis and reduced-order modeling of high-speed stall flow phenomena, DLRK 2021, Aug 2021, Bremen, virtual
• Machine learning-aided CFD with OpenFOAM and PyTorch, training at 16th OpenFOAM Workshop, Jun 2021, Dublin, virtual
• Machine learning-aided CFD with OpenFOAM and PyTorch, SSD Seminar - invited talk, Jun 2021, RWTH Aachen, virtual
• Sparse Spatial Samling - S³, AIAA SciTech Forum, Jan 2021, Nashville, virtual
• Creating data-driven workflows with OpenFOAM and PyTorch, 8th ESI OpenFOAM conference, Oct 2020, Berlin, virtual
• An introduction to supervised learning by example: path regime classification, internal training, Aug 2020, Braunschweig, virtual
• A hybrid approach to compute convection-dominated mass transfer at rising bubbles, 4th GOFUN, Apr 2020, Braunschweig, virtual
• Modeling and simulation of convection-dominated species transfer at rising bubbles, Ph.D. defense, Jan 2020, Darmstadt
• A brief introduction to machine learning and its potential application to CFD, 14th OpenFOAM workshop, Jul 2019, Duisburg
• Data-driven subgrid-scale modeling for convection-dominated concentration boundary layers, 14th OpenFOAM workshop, Jul 2019, Duisburg

Supervised student projects

• Learning of optimized multigrid solver settings for CFD applications, Master thesis, Janis Geise, 2023, Github
• A comparison of reduced-order models for wing buffet predictions, course project, Anton Schreiber, 2023, Github
• Prediction of ice formation on heat exchanger surfaces and defrost control using machine learning, master project, together with Coldsense, Tom Krogmann, 2023
• Robust model-based deep reinforcement learning for flow control, course project, Janis Geise, 2023, Github
• Optimal sensor placement for active flow control with deep reinforcement learning, course project, Tom Krogmann, 2022, Github
• Design and implementation of a data-driven wall function for the velocity in RANS simulations, master project, Jihoo Kang, 2022, Github
• POD-based parametric reduced-order models for time-dependent fluid flows, course project, Jan Schlüter, 2022
• Model-based reinforcement learning for accelerated learning from CFD simulations, course project, Jan Erik Schulze, 2022, Github
• Reduced-order modeling based on cluster-based network modeling applied to the latent variables of an autoencoder, course project, Niels Formella, 2021, Github
• Active control of the flow past a cylinder under Reynolds number variation using deep reinforcement learning, Bachelor thesis, Fabian Gabriel, 2021, Github
• Numerical investigation of 2D transonic shock-buffet around a NACA 0012-34 airfoil using OpenFOAM and flowTorch, course project, Tushar Anil Gholap, 2021, Github
• Active flow control in simulations of fluid flows based on deep reinforcement learning, course project, Darshan Thummar, 2021, Github
• Simulation of Fluid Flows based on the Data-driven Evolution of Vortex Particles, Master thesis, Vemburaj Chockalingam Yadav, 2021, Github
• Datenbasierte Subgridskalen-Modellierung reaktiver Konzentrationsgrenzschichten an freiaufsteigenden Einzelblasen, Master thesis, Alexander Kiefer, 2020
• A comparative study of different mesh types for transport processes near gas bubbles regarding accuracy, stability, and run time, Bachelor thesis, Jan-Alexander Kleikemper, 2018
• Numerical simulation of single rising bubbles influenced by soluble surfactant in the spherical and ellipsoidal regime, Master thesis, Matthias Steinhausen, 2018
• Numerical simulation of reactive species transfer at a spherical gas bubble, Bachelor thesis, Tim Jeremy Patrick Karpowski, 2017