Browsing by Author "Heinemann, Moritz"

Now showing 1 - 4 of 4

Open Access
Accelerated 2D visualization using adaptive resolution scaling and temporal reconstruction
(2023) Becher, Michael; Heinemann, Moritz; Marmann, Thomas; Reina, Guido; Weiskopf, Daniel; Ertl, Thomas
Data visualization relies on efficient rendering to allow users to interactively explore and understand their data. However, achieving interactive frame rates is often challenging, especially for high-resolution displays or large datasets. In computer graphics, several methods temporally reconstruct full-resolution images from multiple consecutive lower-resolution frames. Besides providing temporal image stability, they amortize the rendering costs over multiple frames and thus improve the minimum frame rate. We present a method that adopts this idea to accelerate 2D information visualization, without requiring any changes to the rendering itself. By exploiting properties of orthographic projection, our method significantly improves rendering performance while minimizing the loss of image quality during camera manipulation. For static scenes, it quickly converges to the full-resolution image. We discuss the characteristics and different modes of our method concerning rendering performance and image quality and the corresponding trade-offs. To improve ease of use, we provide automatic resolution scaling in our method to adapt to user-defined target frame rate. Finally, we present extensive rendering benchmarks to examine real-world performance for examples of parallel coordinates and scatterplot matrix visualizations, and discuss appropriate application scenarios and contraindications for usage.
Open Access
ML-based visual analysis of droplet behaviour in multiphase flow simulations
(2018) Heinemann, Moritz
Modern multiphase flow solvers can simulate flows with increasing domain size and precision. This produces large simulation results which need to be analyzed, and to this end visualized. Because of the amount of data, classical visualization approaches become more and more unfitting. Therefore, it is hard to find interesting regions because of visual clutter which is produced by too much data. One solution could be semi automatic assistance systems to support the observer of the visualization. Over the last years, machine learning has grown to a widely researched area. Development not only brought many use cases in research and industry, but highly sophisticated programming frameworks. This makes it much easier to use machine learning in a wide area of applications, such as visualization. In this work we are interested in analyzing multiphase simulations with thousands of droplets. We use machine learning to train artificial neural networks with the droplet data gained from simulations. These trained models are used for finding interesting droplet behavior in the simulation, which is then visualized. Our trained models can predict the development of physical properties and quantities over time, and therefore errors in prediction can guide us to areas of interest which then can be investigated further. The prediction error is visualized as colored dots directly within the 3D simulation dataset using ParaView. Additionally we can plot the properties and their predictions of single droplets over time and show the prediction error separated by property within a spider chart. Finally we show the results, which cover an evaluation of the learning process and an analysis of the used datasets with our method, as well as give an outlook on possible improvement in future work.
Open Access
Power overwhelming : the one with the oscilloscopes
(2024) Gralka, Patrick; Müller, Christoph; Heinemann, Moritz; Reina, Guido; Weiskopf, Daniel; Ertl, Thomas
Visualization as a discipline has to investigate its practical implications in a world steadily moving toward greener computing methods. Quantifying the power consumption of visualization algorithms is thus essential, given the ever-increasing energy needs of GPUs. Previous approaches rely on integrated sensors or invasive methods that require modifications and special test setups. However, they still suffer from imprecision from low sampling rates and integration over time. Using a high-precision, high-frequency setup via steerable oscilloscopes, we can objectively measure the resulting quality of previous approaches. This is essential to establish a ground truth, pave the way for improved modeling of power consumption in general, and enable better estimates based on the output of lower-quality sensors. We finally discuss benefits that can be drawn from the additional insight of the higher-precision setup and which additional use cases can justify the incurred costs.
Open Access
Visual analysis of droplet dynamics in large-scale multiphase spray simulations
(2021) Heinemann, Moritz; Frey, Steffen; Tkachev, Gleb; Straub, Alexander; Sadlo, Filip; Ertl, Thomas
We present a data-driven visual analysis approach for the in-depth exploration of large numbers of droplets. Understanding droplet dynamics in sprays is of interest across many scientific fields for both simulation scientists and engineers. In this paper, we analyze large-scale direct numerical simulation datasets of the two-phase flow of non-Newtonian jets. Our interactive visual analysis approach comprises various dedicated exploration modalities that are supplemented by directly linking to ParaView. This hybrid setup supports a detailed investigation of droplets, both in the spatial domain and in terms of physical quantities. Considering a large variety of extracted physical quantities for each droplet enables investigating different aspects of interest in our data. To get an overview of different types of characteristic behaviors, we cluster massive numbers of droplets to analyze different types of occurring behaviors via domain-specific pre-aggregation, as well as different methods and parameters. Extraordinary temporal patterns are of high interest, especially to investigate edge cases and detect potential simulation issues. For this, we use a neural network-based approach to predict the development of these physical quantities and identify irregularly advected droplets.