activity
20182022
most citedUsing Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows

10 citations · 26 across the 7 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

cs.LG201910 cited

Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows

Mathis Bode, Michael Gauding, Zeyu Lian +5

Turbulence is still one of the main challenges for accurately predicting reactive flows. Therefore, the development of new turbulence closures which can be applied to combustion pr…

physics.flu-dyn2019

The Role of Differential Diffusion during Early Flame Kernel Development under Engine Conditions -- Part II: Effect of Flame Structure and Geometry

Tobias Falkenstein, Hongchao Chu, Mathis Bode +2

From experimental spark ignition (SI) engine studies, it is known that the slow-down of early flame kernel development caused by the ()-property of common transporta…

physics.flu-dyn2019

The Role of Differential Diffusion during Early Flame Kernel Development under Engine Conditions -- Part I: Analysis of the Heat-Release-Rate Response

Tobias Falkenstein, Aleksandra Rezchikova, Raymond Langer +3

Although experimental evidence for the correlation between early flame kernel development and cycle-to-cycle variations (CCV) in spark ignition (SI) engines was provided long ago,…

physics.comp-ph20193 cited

Deep learning at scale for subgrid modeling in turbulent flows

Mathis Bode, Michael Gauding, Konstantin Kleinheinz +1

Modeling of turbulent flows is still challenging. One way to deal with the large scale separation due to turbulence is to simulate only the large scales and model the unresolved co…

physics.flu-dyn2019

DNS Study of the Global Heat Release Rate During Early Flame Kernel Development under Engine Conditions

Tobias Falkenstein, Seongwon Kang, Liming Cai +2

Despite the high technical relevance of early flame kernel development for the reduction of cycle-to-cycle variations in spark ignition engines, there is still a need for a better…

cs.LG2019

A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

Sumedh Yadav, Mathis Bode

A scalable graphical method is presented for selecting, and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is requ…