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Herkert","P. Buchfink","B. Haasdonk","J. Rettberg","J. Fehr" ], "authors": [ {"first" : "R.", "last" : "Herkert"}, {"first" : "P.", "last" : "Buchfink"}, {"first" : "B.", "last" : "Haasdonk"}, {"first" : "J.", "last" : "Rettberg"}, {"first" : "J.", "last" : "Fehr"} ], "editor": [ "Ivan Lirkov","Svetozar Margenov" ], "editors": [ {"first" : "Ivan", "last" : "Lirkov"}, {"first" : "Svetozar", "last" : "Margenov"} ], "pages": "99--107","abstract": "Simulations of large scale dynamical systems in multi-query or real-time contexts require efficient surrogate modelling techniques, as e.g. achieved via Model Order Reduction (MOR). Recently, symplectic methods like the complex singular value decomposition (cSVD) or the SVD-like decomposition have been developed for preserving Hamiltonian structure during MOR. In this contribution, we show how symplectic structure preserving basis generation can be made more efficient with randomized matrix factorizations. We present a randomized complex SVD (rcSVD) algorithm and a randomized SVD-like decomposition (rSVD-like). We demonstrate the efficiency of the approaches with numerical experiments on high dimensional systems.", "isbn" : "978-3-031-56208-2", "bibtexKey": "10.1007/978-3-031-56208-2_9" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/20d96128b61e1e64fe07a0889834cf99c/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-5","PN6-5(II)","selected" ], "intraHash" : "0d96128b61e1e64fe07a0889834cf99c", "interHash" : "23bb54fcd793f1ff09321766c81ad8c0", "label" : "Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking", "user" : "testusersimtech", "description" : "", "date" : "2025-01-27 13:09:00", "changeDate" : "2025-01-27 13:09:00", "count" : 6, "pub-type": "inproceedings", "booktitle": "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", "year": "2021", "url": "", "author": [ "Dirk Väth","Pascal Tilli","Ngoc Thang Vu" ], "authors": [ {"first" : "Dirk", "last" : "Väth"}, {"first" : "Pascal", "last" : "Tilli"}, {"first" : "Ngoc Thang", "last" : "Vu"} ], "pages": "114--123", "bibtexKey": "vath2021beyond" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2baadf322b0b79fe1bc38454c418b0375/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-2","selected" ], "intraHash" : "baadf322b0b79fe1bc38454c418b0375", "interHash" : "a3ae90dbf6cd17e3488488dffffd0934", "label" : "Learning Free-Surface Flow with Physics-Informed Neural Networks", "user" : "testusersimtech", "description" : "", "date" : "2025-01-27 13:09:00", "changeDate" : "2025-01-27 13:09:00", "count" : 10, "pub-type": "inproceedings", "booktitle": "2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)", "year": "2021", "url": "", "author": [ "Raphael Leiteritz","Marcel Hurler","Dirk Pflüger" ], "authors": [ {"first" : "Raphael", "last" : "Leiteritz"}, {"first" : "Marcel", "last" : "Hurler"}, {"first" : "Dirk", "last" : "Pflüger"} ], "pages": "1664-1669","abstract": "The interface between data-driven learning methods and classical simulation poses an interesting field offering a multitude of new applications. In this work, we build on the notion of physics-informed neural networks (PINNs) and employ them in the area of shallow-water equation (SWE) models. These models play an important role in modeling and simulating free-surface flow scenarios such as in flood-wave propagation or tsunami waves. Different formulations of the PINN residual are compared to each other and multiple optimizations are being evaluated to speed up the convergence rate. We test these with different 1-D and 2-D experiments and finally demonstrate that regarding a SWE scenario with varying bathymetry, the method is able to produce competitive results in comparison to the direct numerical simulation with a total relative $L_2$ error of $8.9e-3$.", "isbn" : "978-1-6654-4337-1", "doi" : "10.1109/ICMLA52953.2021.00266", "bibtexKey": "leiteritz2021learning" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2a935bae2b08351be41efd97fae95465e/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-5","PN6-5(II)","misc" ], "intraHash" : "a935bae2b08351be41efd97fae95465e", "interHash" : "ae2771c0b5955a966fc1cef34a99462e", "label" : "Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy", "user" : "testusersimtech", "description" : "", "date" : "2025-01-27 13:09:00", "changeDate" : "2025-01-27 13:09:00", "count" : 5, "pub-type": "inproceedings", "booktitle": "2022 IEEE Spoken Language Technology Workshop (SLT)", "year": "2023", "url": "", "author": [ "Sarina Meyer","Pascal Tilli","Pavel Denisov","Florian Lux","Julia Koch","Ngoc Thang Vu" ], "authors": [ {"first" : "Sarina", "last" : "Meyer"}, {"first" : "Pascal", "last" : "Tilli"}, {"first" : "Pavel", "last" : "Denisov"}, {"first" : "Florian", "last" : "Lux"}, {"first" : "Julia", "last" : "Koch"}, {"first" : "Ngoc Thang", "last" : "Vu"} ], "pages": "912--919", "bibtexKey": "meyer2023anonymizing" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2884f529f6a889cf4e38b853501114fde/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-2","misc" ], "intraHash" : "884f529f6a889cf4e38b853501114fde", "interHash" : "9b7ef067104bc4e2fe485fbeae5721a3", "label" : "A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps", "user" : "testusersimtech", "description" : "", "date" : "2025-01-27 13:09:00", "changeDate" : "2025-01-27 13:09:00", "count" : 9, "pub-type": "misc", "year": "2022", "url": "", "author": [ "Raphael Leiteritz","Kyle Davis","Miriam Schulte","Dirk Pflüger" ], "authors": [ {"first" : "Raphael", "last" : "Leiteritz"}, {"first" : "Kyle", "last" : "Davis"}, {"first" : "Miriam", "last" : "Schulte"}, {"first" : "Dirk", "last" : "Pflüger"} ], "language" : "eng", "doi" : "10.48550/arXiv.2203.14961", "bibtexKey": "leiteritz2022learning" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/26602e5aa5fe8cbb6a32d0e8e55d91594/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-2","misc" ], "intraHash" : "6602e5aa5fe8cbb6a32d0e8e55d91594", "interHash" : "689cf81ddf5b7267aac7ca56118e081a", "label" : "Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps", "user" : "testusersimtech", "description" : "", "date" : "2025-01-27 13:09:00", "changeDate" : "2025-01-27 13:09:00", "count" : 8, "pub-type": "misc", "year": "2023", "url": "", "author": [ "Kyle Davis","Raphael Leiteritz","Dirk Pflüger","Miriam Schulte" ], "authors": [ 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Fehr" ], "authors": [ {"first" : "Robin", "last" : "Herkert"}, {"first" : "Patrick", "last" : "Buchfink"}, {"first" : "Bernard", "last" : "Haasdonk"}, {"first" : "Johannes", "last" : "Rettberg"}, {"first" : "Jörg", "last" : "Fehr"} ], "eprint" : "2405.10465", "archiveprefix" : "arXiv", "primaryclass" : "math.NA", "bibtexKey": "herkert2024erroranalysisrandomizedsymplectic" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2e8ebd2f42b453167d2065725486828cb/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-2(II)","misc" ], "intraHash" : "e8ebd2f42b453167d2065725486828cb", "interHash" : "bfbc52cd98d241445f5051b284bf6ded", "label" : "Evaluation of SYCL\u2019s Different Data Parallel Kernels", "user" : "testusersimtech", "description" : "", "date" : "2025-01-27 13:09:00", "changeDate" : "2025-01-27 13:09:00", "count" : 10, "pub-type": "inproceedings", "booktitle": "Proceedings of the 12th International Workshop on OpenCL and SYCL","series": "IWOCL '24","publisher":"Association for Computing Machinery","address":"New York, NY, USA", "year": "2024", "url": "https://doi.org/10.1145/3648115.3648130", "author": [ "Marcel Breyer","Alexander Van Craen","Dirk Pflüger" ], "authors": [ {"first" : "Marcel", "last" : "Breyer"}, {"first" : "Alexander", "last" : "Van Craen"}, {"first" : "Dirk", "last" : "Pflüger"} ], "pages": "1-4","abstract": "SYCL provides programmers with four, and in the case of AdaptiveCpp even five, ways for calling and writing a device kernel. This paper analyzes the performance of these diverse kernel invocation types for DPC++ and AdaptiveCpp as SYCL implementations on an NVIDIA A100 GPU, an AMD Instinct MI210 GPU, and a dual-socket AMD EPYC 9274F CPU. Using the example of a kernel matrix assembly, we show why the performance can differ by a factor of 100 in the worst case on the same hardware for the same problem using different SYCL implementations and kernel invocation types.", "isbn" : "9798400717901", "language" : "english", "numpages" : "4", "articleno" : "10", "location" : "Chicago, IL, USA", "doi" : "10.1145/3648115.3648130", "bibtexKey": "breyer2024evaluation" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2570940448c9ea9acf382f1b23af082e5/testusersimtech", "tags" : [ "EXC2075","PN6","PN6A-1","selected" ], "intraHash" : "570940448c9ea9acf382f1b23af082e5", "interHash" : "2de849013d296335d6ed8fecf19872ad", "label" : "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 10, "pub-type": "article", "journal": "npj Comput. Mater.", "year": "2024", "url": "http://dx.doi.org/10.1038/s41524-024-01254-1", "author": [ "Viktor Zaverkin","David Holzmüller","Henrik Christiansen","Federico Errica","Francesco Alesiani","Makoto Takamoto","Mathias Niepert","Johannes Kästner" ], "authors": [ {"first" : "Viktor", "last" : "Zaverkin"}, {"first" : "David", "last" : "Holzmüller"}, {"first" : "Henrik", "last" : "Christiansen"}, {"first" : "Federico", "last" : "Errica"}, {"first" : "Francesco", "last" : "Alesiani"}, {"first" : "Makoto", "last" : "Takamoto"}, {"first" : "Mathias", "last" : "Niepert"}, {"first" : "Johannes", "last" : "Kästner"} ], "volume": "10","pages": "83", "doi" : "10.1038/s41524-024-01254-1", "bibtexKey": "zav24" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f50540ba40c21f5bcea6753bf800438f/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-13","selected" ], "intraHash" : "f50540ba40c21f5bcea6753bf800438f", "interHash" : "b4615a71979f90c7965acb30d2f640ea", "label" : "Near-optimal solutions of constrained learning problems", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 9, "pub-type": "inproceedings", "booktitle": "International Conference on Learning Representations~(ICLR)", "year": "2024", "url": "", "author": [ "Juan Elenter","Luiz F. O. Chamon","Alejandro Ribeiro" ], "authors": [ {"first" : "Juan", "last" : "Elenter"}, {"first" : "Luiz F. O.", "last" : "Chamon"}, {"first" : "Alejandro", "last" : "Ribeiro"} ], "arxiv" : "https://arxiv.org/abs/2403.11844", "bibtexKey": "Elenter24n" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/28b2c9006e55a886ca79f64b1302c9e1c/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-6","selected","unclear" ], "intraHash" : "8b2c9006e55a886ca79f64b1302c9e1c", "interHash" : "c658c4630195f65d590c2bf52b163201", "label" : "Local Prediction Models for Spatiotemporal Volume Visualization", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 10, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics", "year": "2019", "url": "http://ieeexplore.ieee.org/document/8941308", "author": [ "Gleb Tkachev","Steffen Frey","Thomas Ertl" ], "authors": [ {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"} ], "note": "Seitenzahlen, Volume?","abstract": "We present a machine learning-based approach for detecting and visualizing complex behavior in spatiotemporal volumes. For this, we train models to predict future data values at a given position based on the past values in its neighborhood, capturing common temporal behavior in the data. We then evaluate the model's prediction on the same data. High prediction error means that the local behavior was too complex, unique or uncertain to be accurately captured during training, indicating spatiotemporal regions with interesting behavior. By training several models of varying capacity, we are able to detect spatiotemporal regions of various complexities. We aggregate the obtained prediction errors into a time series or spatial volumes and visualize them together to highlight regions of unpredictable behavior and how they differ between the models. We demonstrate two further volumetric applications: adaptive timestep selection and analysis of ensemble dissimilarity. We apply our technique to datasets from multiple application domains and demonstrate that we are able to produce meaningful results while making minimal assumptions about the underlying data.", "issn" : "2160-9306", "doi" : "10.1109/TVCG.2019.2961893", "bibtexKey": "tkachev2019local" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/240df3a76ecfe1fbbd3b2f760ce0f705b/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-6","selected" ], "intraHash" : "40df3a76ecfe1fbbd3b2f760ce0f705b", "interHash" : "053c5fbdc956bb823f60e761099d5e74", "label" : "2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 5, "pub-type": "article", "journal": "IEEE Computer Graphics and Applications", "year": "2021", "url": "https://ieeexplore.ieee.org/document/9126163", "author": [ "Karsten Schatz","Christoph Müller","Patrick Gralka","Moritz Heinemann","Alexander Straub","Christoph Schulz","Matthias Braun","Tobias Rau","Michael Becher","Steffen Frey","Guido Reina","Michael Sedlmair","Daniel Weiskopf","Thomas Ertl","Patrick Diehl","Dominic Marcello","Juhan Frank","Thomas Müller" ], "authors": [ {"first" : "Karsten", "last" : "Schatz"}, {"first" : "Christoph", "last" : "Müller"}, {"first" : "Patrick", "last" : "Gralka"}, {"first" : "Moritz", "last" : "Heinemann"}, {"first" : "Alexander", "last" : "Straub"}, {"first" : "Christoph", "last" : "Schulz"}, {"first" : "Matthias", "last" : "Braun"}, {"first" : "Tobias", "last" : "Rau"}, {"first" : "Michael", "last" : "Becher"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Guido", "last" : "Reina"}, {"first" : "Michael", "last" : "Sedlmair"}, {"first" : "Daniel", "last" : "Weiskopf"}, {"first" : "Thomas", "last" : "Ertl"}, {"first" : "Patrick", "last" : "Diehl"}, {"first" : "Dominic", "last" : "Marcello"}, {"first" : "Juhan", "last" : "Frank"}, {"first" : "Thomas", "last" : "Müller"} ], "volume": "41","number": "6","pages": "101-110","abstract": "Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a preprocessing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.", "doi" : "10.1109/MCG.2020.3004613", "bibtexKey": "schatz:2021:contest" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/287f3a38e8d43c2711bff361e7c7e8a59/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-6","slected" ], "intraHash" : "87f3a38e8d43c2711bff361e7c7e8a59", "interHash" : "347d569c3aae7cd4934c93a157da3cfe", "label" : "S4: Self-Supervised learning of Spatiotemporal Similarity", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 11, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics", "year": "2021", "url": "", "author": [ "Gleb Tkachev","Steffen Frey","Thomas Ertl" ], "authors": [ {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"} ], "abstract": "We introduce an ML-driven approach that enables interactive example-based queries for similar behavior in ensembles of spatiotemporal scientific data. This addresses an important use case in the visual exploration of simulation and experimental data, where data is often large, unlabeled and has no meaningful similarity measures available. We exploit the fact that nearby locations often exhibit similar behavior and train a Siamese Neural Network in a self-supervised fashion, learning an expressive latent space for spatiotemporal behavior. This space can be used to find similar behavior with just a few user-provided examples. We evaluate this approach on several ensemble datasets and compare with multiple existing methods, showing both qualitative and quantitative results.", "isbn" : "10.1109/TVCG.2021.3101418", "bibtexKey": "tkachev2021selfsupervised" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/234d00e0acde35fc4ce22d37813ff97af/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-6","misc","unclear" ], "intraHash" : "34d00e0acde35fc4ce22d37813ff97af", "interHash" : "b09504f62022e37c542d6dcaaf5a29a5", "label" : "Evaluation and Selection of Autoencoders for Expressive Dimensionality Reduction of Spatial Ensembles", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 6, "pub-type": "inproceedings", "booktitle": "International Symposium on Visual Computing", "year": "2021", "url": "", "author": [ "Hamid Gadirov","Gleb Tkachev","Thomas Ertl","Steffen Frey" ], "authors": [ {"first" : "Hamid", "last" : "Gadirov"}, {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Thomas", "last" : "Ertl"}, {"first" : "Steffen", "last" : "Frey"} ], "pages": "222--234","note": "völlig unvollständig", "bibtexKey": "gadirov2021evaluation" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f9c37966a7bf1c92e22dba418cfbb016/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-6","selected" ], "intraHash" : "f9c37966a7bf1c92e22dba418cfbb016", "interHash" : "a75bff1510508e53911fd53cb9220bb4", "label" : "Hybrid image processing approach for autonomous crack area detection and tracking using local digital image correlation results applied to single-fiber interfacial debonding", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 5, "pub-type": "article", "journal": "Engineering Fracture Mechanics","publisher":"Elsevier Science", "year": "2019", "url": "", "author": [ "Ilyass Tabiai","Gleb Tkachev","Patrick Diehl","Steffen Frey","Thomas Ertl","Daniel Therriault","Martin Levesque" ], "authors": [ {"first" : "Ilyass", "last" : "Tabiai"}, {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Patrick", "last" : "Diehl"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Thomas", "last" : "Ertl"}, {"first" : "Daniel", "last" : "Therriault"}, {"first" : "Martin", "last" : "Levesque"} ], "volume": "216","pages": "106485", "research-areas" : "Mechanics", "language" : "eng", "issn" : "0013-7944 and 1873-7315", "affiliation" : "Levesque, M (Reprint Author), Polytech Montreal, Lab Multiscale Mech, Montreal, PQ, Canada.\n Tabiai, Ilyass; Diehl, Patrick; Therriault, Daniel; Levesque, Martin, Polytech Montreal, Lab Multiscale Mech, Montreal, PQ, Canada.\n Tkachev, Gleb; Frey, Steffen; Ertl, Thomas, Univ Stuttgart, Visualizat Res Ctr, Stuttgart, Germany.\n Diehl, Patrick, Louisiana State Univ, Ctr Computat & Technol, Baton Rouge, LA 70803 USA.", "unique-id" : "ISI:000477573000020", "doi" : "10.1016/j.engfracmech.2019.106485", "bibtexKey": "tabiai2019hybrid" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c6da4f3866d9e6f3082cec3bf9c5e06e/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-6","selected" ], "intraHash" : "c6da4f3866d9e6f3082cec3bf9c5e06e", "interHash" : "64664db768157c403bbffc818f9ddb01", "label" : "Visual analysis of droplet dynamics in large-scale multiphase spray simulations", "user" : "testusersimtech", "description" : "", "date" : "2024-08-09 07:49:09", "changeDate" : "2024-08-09 07:49:09", "count" : 11, "pub-type": "article", "journal": "Journal of Visualization", "year": "2021", "url": "https://doi.org/10.1007/s12650-021-00750-6", "author": [ "Moritz Heinemann","Steffen Frey","Gleb Tkachev","Alexander Straub","Filip Sadlo","Thomas Ertl" ], "authors": [ {"first" : "Moritz", "last" : "Heinemann"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Gleb", "last" : "Tkachev"}, {"first" : "Alexander", "last" : "Straub"}, {"first" : "Filip", "last" : "Sadlo"}, {"first" : "Thomas", "last" : "Ertl"} ], "volume": "24","number": "5","pages": "943--961","abstract": "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.", "issn" : "1875-8975", "doi" : "10.1007/s12650-021-00750-6", "bibtexKey": "heinemann2021visual" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2106afe71db48420fb0427e965db64b73/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-4","selected" ], "intraHash" : "106afe71db48420fb0427e965db64b73", "interHash" : "990b4793043d16fcfd50983d0172b5a8", "label" : "VisME software v1.2", "user" : "testusersimtech", "description" : "", "date" : "2024-07-19 15:07:18", "changeDate" : "2024-07-19 15:07:18", "count" : 5, "pub-type": "misc", "publisher":"Zenodo", "year": "2019", "url": "https://zenodo.org/record/3352236", "author": [ "Tanja Munz" ], "authors": [ {"first" : "Tanja", "last" : "Munz"} ], "copyright" : "Creative Commons Attribution 4.0 International", "doi" : "10.5281/ZENODO.3352236", "bibtexKey": "munz2019visme" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2997a23b44db6d6461a5a96269c59d4e0/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-4","selected" ], "intraHash" : "997a23b44db6d6461a5a96269c59d4e0", "interHash" : "715d4bdb3be19cc2b2325c6b08a22593", "label" : "Visualization-based improvement of neural machine translation", "user" : "testusersimtech", "description" : "", "date" : "2024-07-19 15:07:18", "changeDate" : "2024-07-19 15:07:18", "count" : 9, "pub-type": "article", "journal": "Computers & Graphics", "year": "2022", "url": "https://www.sciencedirect.com/science/article/pii/S0097849321002594", "author": [ "Tanja Munz","Dirk Väth","Paul Kuznecov","Ngoc Thang Vu","Daniel Weiskopf" ], "authors": [ {"first" : "Tanja", "last" : "Munz"}, {"first" : "Dirk", "last" : "Väth"}, {"first" : "Paul", "last" : "Kuznecov"}, {"first" : "Ngoc Thang", "last" : "Vu"}, {"first" : "Daniel", "last" : "Weiskopf"} ], "volume": "103","pages": "45-60","abstract": "We introduce a novel visual-interactive approach for analyzing, understanding, and correcting neural machine translation. Our system supports users in automatically translating documents using neural machine translation and identifying and correcting possible erroneous translations. User corrections can then be used to fine-tune the neural machine translation model and automatically improve the whole document. While translation results of neural machine translation can be impressive, there are still many challenges such as over- and under-translation, domain-specific terminology, and handling long sentences, making it necessary for users to verify translation results. Our system aims at supporting users in this task. Our visual analytics approach combines several visualization techniques in an interactive system. A parallel coordinates plot with multiple metrics related to translation quality can be used to find, filter, and select translations that might contain errors. An interactive beam search visualization and graph- or matrix-based visualizations for attention weights can be used for post-editing and understanding machine-generated translations. The machine translation model is updated from user corrections to improve the translation quality of the whole document. We designed our approach for an LSTM-based translation model and extended it to also include the Transformer architecture. We show for representative examples possible mistranslations and how to use our system to deal with them. A user study revealed that many participants favor such a system over manual text-based translation, especially for translating large documents. Furthermore, we performed quantitative computer-based experiments that show that our system can be used to improve translation quality and reduce post-editing efforts for domain-specific documents.", "issn" : "0097-8493", "doi" : "https://doi.org/10.1016/j.cag.2021.12.003", "bibtexKey": "munz2022nmtvis" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f01ef3e7484c3797fc2d63e4c95f0e50/testusersimtech", "tags" : [ "EXC2075","PN6","PN6-11","selected" ], "intraHash" : "f01ef3e7484c3797fc2d63e4c95f0e50", "interHash" : "f546dbf0b915f573e287d02bbbf9b8c2", "label" : "VSA4VQA: Scaling A Vector Symbolic Architecture To Visual Question Answering on Natural Images", "user" : "testusersimtech", "description" : "", "date" : "2024-07-19 15:07:18", "changeDate" : "2024-07-19 15:07:18", "count" : 5, "pub-type": "inproceedings", "booktitle": "Proc. 46th Annual Meeting of the Cognitive Science Society (CogSci)", "year": "2024", "url": "", "author": [ "Anna Penzkofer","Lei Shi","Andreas Bulling" ], "authors": [ {"first" : "Anna", "last" : "Penzkofer"}, {"first" : "Lei", "last" : "Shi"}, {"first" : "Andreas", "last" : "Bulling"} ], "note": "spotlight","abstract": "While Vector Symbolic Architectures (VSAs) are promising for modelling spatial cognition, their application is currently limited to artificially generated images and simple spatial queries. We propose VSA4VQA \u2013 a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). VSA4VQA is the first model to scale a VSA to complex spatial queries. Our method is based on the Semantic Pointer Architecture (SPA) to encode objects in a hyper-dimensional vector space. To encode natural images, we extend the SPA to include dimensions for object\u2019s width and height in addition to their spatial location. To perform spatial queries we further introduce learned spatial query masks and integrate a pre-trained vision-language model for answering attribute-related questions. We evaluate our method on the GQA benchmark dataset and show that it can effectively encode natural images, achieving competitive performance to state-of-the-art deep learning methods for zero-shot VQA.", "code" : "https://git.hcics.simtech.uni-stuttgart.de/public-projects/VSA4VQA", "bibtexKey": "penzkofer24_cogsci" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cdd13dd38e0b03302d9ecb41c4bc7f2a/testusersimtech", "tags" : [ "EXC2075","PN6","darus" ], "intraHash" : "cdd13dd38e0b03302d9ecb41c4bc7f2a", "interHash" : "3ca8f1b169c650ca2cb27512d841fcd4", "label" : "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", "user" : "testusersimtech", "description" : "", "date" : "2024-07-03 10:45:48", "changeDate" : "2024-07-03 10:45:48", "count" : 5, "pub-type": "misc", "year": "2020", "url": "", "author": [ "Timothy Praditia" ], "authors": [ {"first" : "Timothy", "last" : "Praditia"} ], "note": "Related to: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W.: Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies 2020","abstract": "This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs necessary for training, validating, and testing the ANN. The input data consist of CaO density, Ca(OH)2 density, CaO specific heat capacity, Ca(OH)2, porosity, permeability, reaction rate constant, initial and outlet pressure, initial temperature, inlet temperature, N2 molar inflow rate, H2O molar inflow rate, and specific reaction enthalpy. The output data consist of pressure, temperature, CaO volume fraction, and H2O molar fraction. Additionally, there is an automated script file for the DuMuX run.", "affiliation" : "Praditia, Timothy/Universität Stuttgart", "orcid-numbers" : "Praditia, Timothy/0000-0003-3619-9122", "doi" : "10.18419/darus-633", "bibtexKey": "praditia2020inputoutput" } ] }