{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/20d0a260ffdf08f7d114fc508b8e2b33f/kpluhackova", "tags" : [ "myown","simtech","pn3-11","pn4","pn3","pn6","AISA","updated","exc2075" ], "intraHash" : "0d0a260ffdf08f7d114fc508b8e2b33f", "interHash" : "0b1970f7c28aa561666ea785dfb94a98", "label" : "ART-SM: Boosting Fragment-Based Backmapping by Machine Learning", "user" : "kpluhackova", "description" : "", "date" : "2025-05-29 10:53:24", "changeDate" : "2025-11-19 15:19:37", "count" : 5, "pub-type": "article", "journal": "Journal of Chemical Theory and Computation","publisher":"American Chemical Society (ACS)", "year": "2025", "url": "http://dx.doi.org/10.1021/acs.jctc.5c00189", "author": [ "Christian Pfaendner","Viktoria Korn","Pritom Gogoi","Benjamin Unger","Kristyna Pluhackova" ], "authors": [ {"first" : "Christian", "last" : "Pfaendner"}, {"first" : "Viktoria", "last" : "Korn"}, {"first" : "Pritom", "last" : "Gogoi"}, {"first" : "Benjamin", "last" : "Unger"}, {"first" : "Kristyna", "last" : "Pluhackova"} ], "volume": "21","number": "8","pages": "4151\u20134166", "issn" : "1549-9626", "doi" : "10.1021/acs.jctc.5c00189", "bibtexKey": "Pfaendner_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/20c5ab2bb1841f0df1cef81bab5d3cf99/grigaud", "tags" : [ "myown","simtech","pn6","updated","exc2075","pn6-9" ], "intraHash" : "0c5ab2bb1841f0df1cef81bab5d3cf99", "interHash" : "ec0c2ad15f55791c070b356fd971816b", "label" : "A class of regularizations based on nonlinear isotropic diffusion for inverse problems", "user" : "grigaud", "description" : "", "date" : "2023-10-30 10:56:25", "changeDate" : "2023-10-30 10:58:24", "count" : 8, "pub-type": "article", "journal": "IMA Journal of Numerical Analysis", "year": "2023", "url": "https://doi.org/10.1093/imanum/drad002", "author": [ "Bernadette N Hahn","Gaël Rigaud","Richard Schmähl" ], "authors": [ {"first" : "Bernadette N", "last" : "Hahn"}, {"first" : "Gaël", "last" : "Rigaud"}, {"first" : "Richard", "last" : "Schmähl"} ], "note": "drad002","abstract": "Building on the well-known total variation, this paper develops a general regularization technique based on nonlinear isotropic diffusion (NID) for inverse problems with piecewise smooth solutions. The novelty of our approach is to be adaptive (we speak of A-NID), i.e., the regularization varies during the iterates in order to incorporate prior information on the edges, deal with the evolution of the reconstruction and circumvent the limitations due to the nonconvexity of the proposed functionals. After a detailed analysis of the convergence and well-posedness of the method, the latter is validated by simulations performed on synthetic and real data on computerized tomography.", "eprint" : "https://academic.oup.com/imajna/advance-article-pdf/doi/10.1093/imanum/drad002/49332831/drad002.pdf", "issn" : "0272-4979", "doi" : "10.1093/imanum/drad002", "bibtexKey": "10.1093/imanum/drad002" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/250208e77b4197b9a139b28516b45c467/mschmitt", "tags" : [ "postPrint","graduateSchool","SimTech","graduateschool","peerReviewed","pn6","prePrint","EXC2075","exc2075" ], "intraHash" : "50208e77b4197b9a139b28516b45c467", "interHash" : "7efbe1d85ce6665f0065010c27e413a7", "label" : "Meta-Uncertainty in Bayesian Model Comparison", "user" : "mschmitt", "description" : "", "date" : "2023-04-20 13:53:02", "changeDate" : "2023-04-27 21:12:24", "count" : 2, "pub-type": "inproceedings", "booktitle": "Proceedings of The 26th International Conference on Artificial Intelligence and Statistics","series": "Proceedings of Machine Learning Research","publisher":"PMLR", "year": "2023", "url": "https://proceedings.mlr.press/v206/schmitt23a.html", "author": [ "Marvin Schmitt","Stefan T. Radev","Paul-Christian Bürkner" ], "authors": [ {"first" : "Marvin", "last" : "Schmitt"}, {"first" : "Stefan T.", "last" : "Radev"}, {"first" : "Paul-Christian", "last" : "Bürkner"} ], "volume": "206","pages": "11--29","abstract": "Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models. In standard BMC, we construct a discrete probability distribution over the set of possible models, conditional on the observed data of interest. These posterior model probabilities (PMPs) are measures of uncertainty, but\u2014when derived from a finite number of observations\u2014are also uncertain themselves. In this paper, we conceptualize distinct levels of uncertainty which arise in BMC. We explore a fully probabilistic framework for quantifying meta-uncertainty, resulting in an applied method to enhance any BMC workflow. Drawing on both Bayesian and frequentist techniques, we represent the uncertainty over the uncertain PMPs via meta-models which combine simulated and observed data into a predictive distribution for PMPs on new data. We demonstrate the utility of the proposed method in the context of conjugate Bayesian regression, likelihood-based inference with Markov chain Monte Carlo, and simulation-based inference with neural networks.", "bibtexKey": "schmitt2023metauncertainty" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/267e6bed0c516c94cc12f5ccdec159db0/grigaud", "tags" : [ "myown","SimTech","PN6","PN6-9","EXC2075","updated" ], "intraHash" : "67e6bed0c516c94cc12f5ccdec159db0", "interHash" : "bf067aaf9935a678f03455bb81f2d5a3", "label" : "Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods", "user" : "grigaud", "description" : "", "date" : "2023-02-22 14:17:12", "changeDate" : "2023-10-30 10:49:04", "count" : 6, "pub-type": "article", "journal": "Inverse Problems","publisher":"IOP Publishing", "year": "2023", "url": "https://doi.org/10.1088%2F1361-6420%2Facb2ed", "author": [ "Janek Gödeke","Gaël Rigaud" ], "authors": [ {"first" : "Janek", "last" : "Gödeke"}, {"first" : "Gaël", "last" : "Rigaud"} ], "volume": "39","number": "3","pages": "034004", "doi" : "10.1088/1361-6420/acb2ed", "bibtexKey": "G_deke_2023" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2453d3bb69482a437ac0b847ebf80fd9f/kpluhackova", "tags" : [ "myown","PN3-11","simtech","PN3","peerReviewed","PN6","PN6-7","EXC2075","updated" ], "intraHash" : "453d3bb69482a437ac0b847ebf80fd9f", "interHash" : "db4618ba1f487d43517baad51a0d6bdb", "label" : "Multiple pore lining residues modulate water permeability of GlpF", "user" : "kpluhackova", "description" : "", "date" : "2022-11-23 19:07:57", "changeDate" : "2023-11-16 09:35:25", "count" : 6, "pub-type": "article", "journal": "Protein Science", "year": "2022", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4431", "author": [ "Kristyna Pluhackova","Valentin Schittny","Paul-Christian Bürkner","Christine Siligan","Andreas Horner" ], "authors": [ {"first" : "Kristyna", "last" : "Pluhackova"}, {"first" : "Valentin", "last" : "Schittny"}, {"first" : "Paul-Christian", "last" : "Bürkner"}, {"first" : "Christine", "last" : "Siligan"}, {"first" : "Andreas", "last" : "Horner"} ], "volume": "31","number": "10","pages": "e4431","abstract": "Abstract The water permeability of aquaporins (AQPs) varies by more than an order of magnitude even though the pore structure, geometry, as well as the channel lining residues are highly conserved. However, channel gating by pH, divalent ions or phosphorylation was only shown for a minority of AQPs. Structural and in silico indications of water flux modulation by flexible side chains of channel lining residues have not been experimentally confirmed yet. Hence, the aquaporin \u201Copen state\u201D is still considered to be a continuously open pore with water molecules permeating in a single-file fashion. Using protein mutations outside the selectivity filter in the aqua(glycerol)facilitator GlpF of Escherichia coli we, to the best of our knowledge, for the first time, modulate the position of the highly conserved Arg in the selectivity filter. This in turn enhances or reduces the unitary water permeability of GlpF as shown in silico by molecular dynamics (MD) simulations and in vitro with purified and reconstituted GlpF. This finding suggests that AQP water permeability can indeed be regulated by lipid bilayer asymmetry and the transmembrane potential. Strikingly, our long-term MD simulations reveal that not only the conserved Arg in the selectivity filter, but the position and dynamics of multiple other pore lining residues modulate water passage through GlpF. This finding is expected to trigger a wealth of future investigations on permeability and regulation of AQPs among others with the aim to tune water permeability for biotechnological applications.", "eprint" : "https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.4431", "doi" : "https://doi.org/10.1002/pro.4431", "bibtexKey": "https://doi.org/10.1002/pro.4431" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c6da4f3866d9e6f3082cec3bf9c5e06e/gtkachev", "tags" : [ "simtech","pn6","EXC2075" ], "intraHash" : "c6da4f3866d9e6f3082cec3bf9c5e06e", "interHash" : "64664db768157c403bbffc818f9ddb01", "label" : "Visual analysis of droplet dynamics in large-scale multiphase spray simulations", "user" : "gtkachev", "description" : "", "date" : "2022-01-18 14:54:19", "changeDate" : "2022-01-18 13:54:19", "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/287f3a38e8d43c2711bff361e7c7e8a59/katharinafuchs", "tags" : [ "myown","simtech","peerReviewed","vis(us)","pn6","visus:ertl","visus:tkachegb","EXC2075","visus","from:gtkachev","visus:freysn" ], "intraHash" : "87f3a38e8d43c2711bff361e7c7e8a59", "interHash" : "347d569c3aae7cd4934c93a157da3cfe", "label" : "S4: Self-Supervised learning of Spatiotemporal Similarity", "user" : "katharinafuchs", "description" : "", "date" : "2021-12-08 17:10:08", "changeDate" : "2021-12-08 16:10:08", "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/28b2c9006e55a886ca79f64b1302c9e1c/katharinafuchs", "tags" : [ "simtech","peerReviewed","vis(us)","pn6","visus:ertl","visus:tkachegb","EXC2075","visus","from:gtkachev","visus:freysn" ], "intraHash" : "8b2c9006e55a886ca79f64b1302c9e1c", "interHash" : "c658c4630195f65d590c2bf52b163201", "label" : "Local Prediction Models for Spatiotemporal Volume Visualization", "user" : "katharinafuchs", "description" : "", "date" : "2021-12-08 17:10:08", "changeDate" : "2021-12-08 16:10:08", "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"} ], "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/287f3a38e8d43c2711bff361e7c7e8a59/gtkachev", "tags" : [ "myown","simtech","visus:ertl","EXC2075","visus","peerReviewed","vis(us)","pn6","visus:tkachegb","visus:freysn" ], "intraHash" : "87f3a38e8d43c2711bff361e7c7e8a59", "interHash" : "347d569c3aae7cd4934c93a157da3cfe", "label" : "S4: Self-Supervised learning of Spatiotemporal Similarity", "user" : "gtkachev", "description" : "", "date" : "2021-10-04 19:04:24", "changeDate" : "2021-10-04 17:04:24", "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/28b2c9006e55a886ca79f64b1302c9e1c/gtkachev", "tags" : [ "simtech","visus:ertl","EXC2075","visus","peerReviewed","vis(us)","pn6","visus:tkachegb","visus:freysn" ], "intraHash" : "8b2c9006e55a886ca79f64b1302c9e1c", "interHash" : "c658c4630195f65d590c2bf52b163201", "label" : "Local Prediction Models for Spatiotemporal Volume Visualization", "user" : "gtkachev", "description" : "", "date" : "2020-01-10 13:37:33", "changeDate" : "2021-12-06 11:27:06", "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"} ], "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" } ] }