DSpace Repository :: Browsing by Author "Garth, Christoph"

Browsing by Author "Garth, Christoph"

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Alternative Parameters for On-The-Fly Simplification of MergeTrees
(The Eurographics Association, 2020) Werner, Kilian; Garth, Christoph; Frey, Steffen and Huang, Jian and Sadlo, Filip
Topological simplification of merge trees requires a user specified persistence threshold. As this threshold is based on prior domain knowledge and has an unpredictable relation to output size, its use faces challenges in large-data situations like online, distributed or out-of-core scenarios. We propose two alternative parameters, a targeted percentile size reduction and a total output size limit, to increase flexibility in those scenarios.
Branch Decomposition-Independent Edit Distances for Merge Trees
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Wetzels, Florian; Leitte, Heike; Garth, Christoph; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with O(n) nodes, we describe an O(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.
Decision Boundary Visualization for Counterfactual Reasoning
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Sohns, Jan‐Tobias; Garth, Christoph; Leitte, Heike; Hauser, Helwig and Alliez, Pierre
Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high‐dimensional input space) is quickly exceeded. To explain a model's decisions, black‐box methods have been proposed that provide either non‐linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What‐If'‐reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high‐dimensional hyperplanes—2D‐slices of the high‐dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real‐world datasets and illustrate that it allows identification of instance‐based decision boundary structures and can answer multi‐dimensional ‘What‐If'‐questions, thereby identifying counterfactual scenarios visually.
EuroVis 2020 Short Papers: Frontmatter
(The Eurographics Association, 2020) Kerren, Andreas; Garth, Christoph; Marai, G. Elisabeta; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
EuroVis 2021 Short Papers: Frontmatter
(The Eurographics Association, 2021) Agus, Marco; Garth, Christoph; Kerren, Andreas; Agus, Marco and Garth, Christoph and Kerren, Andreas
Extended Visual Programming for Complex Parallel Pipelines in ParaView
(The Eurographics Association, 2023) Petersen, Marvin; Lukasczyk, Jonas; Gueunet, Charles; Chabat, Timothée; Garth, Christoph; Bujack, Roxana; Pugmire, David; Reina, Guido
Modern visualization software facilitates the creation of visualization pipelines combining a plethora of algorithms to achieve high-fidelity visualization. When the complexity of the pipelines to be created increases, additional techniques are needed to ensure that reasoning about the pipelines structure and its performance remains feasible. This paper presents three additions to ParaView with the goal of improving presentation of complex, parallel pipelines benefiting pipeline realization. More specifically, we provide a runtime performance annotation visualization integrated in a visual programming node editor, allowing all users to reason about basic performance and intuitively manipulate the structure and configuration of pipelines. Further, we extend the list of available filters with control flow filters, supporting for- and while-loops with a comprehensible representation in the node editor. Our extension is based on graphical manipulation of a node graph that expresses the flow of data and computation in a VTK pipeline, and draws upon a long tradition and positive experience with similar interfaces across a wide range of software systems such as the visualization tools SCIRun and VTK Designer, or the rendering systems Blender and Houdini. The extension we provide integrates seamlessly into the existing ParaView architecture as a plug-in, i.e., it does not require any modifications to ParaView itself or VTK's execution model.
Fuzzy Contour Trees: Alignment and Joint Layout of Multiple Contour Trees
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lohfink, Anna-Pia; Wetzels, Florian; Lukasczyk, Jonas; Weber, Gunther H.; Garth, Christoph; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
We describe a novel technique for the simultaneous visualization of multiple scalar fields, e.g. representing the members of an ensemble, based on their contour trees. Using tree alignments, a graph-theoretic concept similar to edit distance mappings, we identify commonalities across multiple contour trees and leverage these to obtain a layout that can represent all trees simultaneously in an easy-to-interpret, minimally-cluttered manner. We describe a heuristic algorithm to compute tree alignments for a given similarity metric, and give an algorithm to compute a joint layout of the resulting aligned contour trees. We apply our approach to the visualization of scalar field ensembles, discuss basic visualization and interaction possibilities, and demonstrate results on several analytic and real-world examples.
Saliency Clouds: Visual Analysis of Point Cloud-oriented Deep Neural Networks in DeepRL for Particle Physics
(The Eurographics Association, 2022) Mulawade, Raju Ningappa; Garth, Christoph; Wiebel, Alexander; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
We develop and describe saliency clouds, that is, visualization methods employing explainable AI methods to analyze and interpret deep reinforcement learning (DeepRL) agents working on point cloud-based data. The agent in our application case is tasked to track particles in high energy physics and is still under development. The point clouds contain properties of particle hits on layers of a detector as the input to reconstruct the trajectories of the particles. Through visualization of the influence of different points, their possible connections in an implicit graph, and other features on the decisions of the policy network of the DeepRL agent, we aim to explain the decision making of the agent in tracking particles and thus support its development. In particular, we adapt gradient-based saliency mapping methods to work on these point clouds. We show how the properties of the methods, which were developed for image data, translate to the structurally different point cloud data. Finally, we present visual representations of saliency clouds supporting visual analysis and interpretation of the RL agent's policy network.
Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps
(The Eurographics Association, 2021) Sane, Sudhanshu; Yenpure, Abhishek; Bujack, Roxana; Larsen, Matthew; Moreland, Kenneth; Garth, Christoph; Johnson, Chris R.; Childs, Hank; Larsen, Matthew and Sadlo, Filip
In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field analysis has recently become an active area of research. However, the current literature is largely limited to theoretical settings and lacks a solution to address scalability of the technique in distributed memory. To improve scalability, we propose and evaluate the benefits and limitations of a simple, yet novel, performance optimization. Our proposed optimization is a communication-free model resulting in local Lagrangian flow maps, requiring no message passing or synchronization between processes, intrinsically improving scalability, and thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from communication overheads. To evaluate our approach, we computed Lagrangian flow maps for four time-varying simulation vector fields and investigated how execution time and reconstruction accuracy are impacted by the number of GPUs per compute node, the total number of compute nodes, particles per rank, and storage intervals. Our study consisted of experiments computing Lagrangian flow maps with up to 67M particle trajectories over 500 cycles and used as many as 2048 GPUs across 512 compute nodes. In all, our study contributes an evaluation of a communication-free model as well as a scalability study of computing distributed Lagrangian flow maps at scale using in situ infrastructure on a modern supercomputer.
State of the Art in Time-Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bujack, Roxana; Yan, Lin; Hotz, Ingrid; Garth, Christoph; Wang, Bei; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
We present a state-of-the-art report on time-dependent flow topology. We survey representative papers in visualization and provide a taxonomy of existing approaches that generalize flow topology from time-independent to time-dependent settings. The approaches are classified based upon four categories: tracking of steady topology, reference frame adaption, pathline classification or clustering, and generalization of critical points. Our unique contributions include introducing a set of desirable mathematical properties to interpret physical meaningfulness for time-dependent flow visualization, inferring mathematical properties associated with selective research papers, and utilizing such properties for classification. The five most important properties identified in the existing literature include coincidence with the steady case, induction of a partition within the domain, Lagrangian invariance, objectivity, and Galilean invariance.
State-of-the-Art Report on Optimizing Particle Advection Performance
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Yenpure, Abhishek; Sane, Sudhanshu; Binyahib, Roba; Pugmire, David; Garth, Christoph; Childs, Hank; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
The computational work to perform particle advection-based flow visualization techniques varies based on many factors, including number of particles, duration, and mesh type. In many cases, the total work is significant, and total execution time (''performance'') is a critical issue. This state-of-the-art report considers existing optimizations for particle advection, using two high-level categories: algorithmic optimizations and hardware efficiency. The sub-categories for algorithmic optimizations include solvers, cell locators, I/O efficiency, and precomputation, while the sub-categories for hardware efficiency all involve parallelism: shared-memory, distributed-memory, and hybrid. Finally, this STAR concludes by identifying current gaps in our understanding of particle advection performance and its optimizations.
A Survey of Seed Placement and Streamline Selection Techniques
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Sane, Sudhanshu; Bujack, Roxana; Garth, Christoph; Childs, Hank; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state-of-the-art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high-level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density-based, feature-based, and similarity-based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.

Browsing by Author "Garth, Christoph"

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