The temporal evolution of physically based fluid simulations is my research topic. I apply machine learning techniques on simulated fluid data and predict the temporal behavior of the physical quantities like pressure or velocity fields.
From 2018 to 2020 I was a research associate in Nils Thuerey’s group.
Besides organizing the Computer Games Laboratory Course I was also supervising Bachelor’s or Master’s Theses. See below for a list of the most recent supervised theses.
E-mail: research (at) steffenwiewel (dot) de
- Steffen Wiewel, Byungsoo Kim, Vinicius C. Azevedo, Barbara Solenthaler and Nils Thuerey, “Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow”, Symposium on Computer Animation 2020, last update March 2020 [Project] [Computer Graphics Forum Volume X – Issue Y (Coming Soon)]
- Steffen Wiewel, Moritz Becher and Nils Thuerey, “Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow”, Eurographics 2019, last update March 2019 [Project] [Computer Graphics Forum Volume 38 – Issue 2]
- Steffen Wiewel, “Advanced Sequence Learning for Liquid Simulations”, M.Sc. Thesis, TUM, March 2018. (English)
- Steffen Wiewel, “Practical Applications of the DirectX 11 Depth-Buffer”, B.Sc. Thesis, TUM, September 2015. (German)
- Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow, Symposium on Computer Animation 2020, October 2020
- Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow, Eurographics Conference 2019, May 2019
- Winter 2019: Deep Learning in Computer Graphics
- Summer 2019: Grundlagen Algorithmen und Datenstrukturen
- Summer 2019: Computer Games Laboratory
- Summer 2018: Computer Games Laboratory
- Moritz Becher, “Transfer Learning for Fluid Simulation”, M.Sc. Thesis, TUM, February 2019. (English)
- Tobias Zengerle, “Physics-based Sequence to Sequence
Networks with Convolutions”, B.Sc. Thesis, TUM, August 2018. (English)