1 to 7 of 7 Results
Mar 13, 2024 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Krake, Tim; Klötzl, Daniel; Hägele, David; Weiskopf, Daniel, 2024, "Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess - Supplemental Material", https://doi.org/10.18419/darus-3845, DaRUS, V1
In this supplemental material, we provide the appendix (mathematically exact propagation of uncertainty) and the video material for uncertainty-aware seasonal-trend decomposition based on loess (UASTL). This material complements the main document: The paper on Uncertainty-Aware S... |
Jun 16, 2023 - Institute for Structural Mechanics
Tkachuk, Anton; Krake, Tim; Gade, Jan; von Scheven, Malte, 2023, "Matlab Implementation of Efficient Computation of Redundancy Matrices", https://doi.org/10.18419/darus-3347, DaRUS, V1
This is a Demo for the manuscript 'Efficient Computation of Redundancy Matrices for Moderately Redundant Truss and Frames Structures' that demonstrates the speedup of the proposed algorithms. The computation is done in single-precision. Please open and run the main file. Further... |
Nov 9, 2022 - Visualisierungsinstitut der Universität Stuttgart
Krake, Tim; Klötzl, Daniel; Eberhardt, Bernhard; Weiskopf, Daniel, 2022, "Constrained Dynamic Mode Decomposition - Supplemental Material", https://doi.org/10.18419/darus-3107, DaRUS, V1
In this supplemental material, we provide additional application results of constrained Dynamic Mode Decomposition and a parameter study for the delay parameter d that complement the evaluation in the main document: The paper on Constrained Dynamic Mode Decomposition published wi... |
Oct 13, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Hägele, David; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/darus-3104, DaRUS, V1
This dataset contains the supplemental material for "Uncertainty-Aware Multidimensional Scaling". Uncertainty-aware multidimensional scaling (UAMDS) is a nonlinear dimensionality reduction technique for sets of random vectors. This dataset consists of a PDF document that contains... |
Oct 12, 2022 - Institute for Structural Mechanics
Krake, Tim; von Scheven, Malte, 2022, "Matlab Implementation of Efficient Updates of Redundancy Matrices", https://doi.org/10.18419/darus-2870, DaRUS, V1
This is a Demo for the manuscript 'Efficient Update of Redundancy Matrices for Truss and Frame Structures' that demonstrates the speedup and accuracy of the proposed update formulas. The computation is done in single-precision. Please open and run the main file. Further informati... |
Sep 21, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Rodrigues, Nils; Schulz, Christoph; Döring, Sören; Baumgartner, Daniel; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution", https://doi.org/10.18419/darus-3055, DaRUS, V1
Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution". Contains: math behind Relaxed Dot Plots additional images pseudo-anonymous study data source code for library and test application To view the material, extract supp... |
Aug 8, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Hägele, David; Krake, Tim, 2022, "Source Code for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/darus-2995, DaRUS, V1
This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language. UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to proje... |