{ "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/2cd0d95c2801c2ffdc85ff3a211a3af2d/simtech", "tags" : [ "EXC2075","PN3","PN6","curated" ], "intraHash" : "cd0d95c2801c2ffdc85ff3a211a3af2d", "interHash" : "f693360d2d45e3f9b26f5cfd569849da", "label" : "Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials", "user" : "simtech", "description" : "", "date" : "2026-03-04 14:23:20", "changeDate" : "2026-04-13 14:57:11", "count" : 5, "pub-type": "article", "journal": "J. Chem. Theory Comput.", "year": "2026", "url": "http://dx.doi.org/10.1021/acs.jctc.5c01767", "author": [ "Moritz R. Schäfer","Johannes Kästner" ], "authors": [ {"first" : "Moritz R.", "last" : "Schäfer"}, {"first" : "Johannes", "last" : "Kästner"} ], "volume": "22","pages": "1947-1957", "doi" : "10.1021/acs.jctc.5c01767", "bibtexKey": "sch26" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/26bbbff2d5e3a13a6fa3605fc73785133/simtech", "tags" : [ "EXC2075","PN6","PN6-5","curated" ], "intraHash" : "6bbbff2d5e3a13a6fa3605fc73785133", "interHash" : "72a33379c480515a2361e7c0f61939f6", "label" : "Beyond scaling curves: internal dynamics of neural networks through the NTK lens", "user" : "simtech", "description" : "", "date" : "2026-02-28 17:13:45", "changeDate" : "2026-04-13 14:57:11", "count" : 5, "pub-type": "article", "journal": "Machine Learning: Science and Technology","publisher":"IOP Publishing", "year": "2026", "url": "https://doi.org/10.1088/2632-2153/ae4442", "author": [ "Konstantin Nikolaou","Sven Krippendorf","Samuel Tovey","Christian Holm" ], "authors": [ {"first" : "Konstantin", "last" : "Nikolaou"}, {"first" : "Sven", "last" : "Krippendorf"}, {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Christian", "last" : "Holm"} ], "volume": "7","number": "2","pages": "025005","abstract": "Scaling laws offer valuable insights into the relationship between neural network (NN) performance and computational cost, yet their underlying mechanisms remain poorly understood. In this work, we empirically analyze how NNs behave under data and model scaling through the lens of the neural tangent kernel. This analysis establishes a link between performance scaling and the internal dynamics of NNs. Our findings of standard vision tasks show that similar performance scaling exponents can occur even though the internal model dynamics show opposite behavior. This demonstrates that performance scaling alone is insufficient for understanding the underlying mechanisms of NNs. We also address a previously unresolved issue in neural scaling: how convergence to the infinite-width limit affects scaling behavior in finite-width models. To this end, we investigate how feature learning is lost as the model width increases and quantify the transition between kernel-driven and feature-driven scaling regimes. We identify the maximum model width that supports feature learning, which, in our setups, we find to be more than ten times smaller than typical large language model widths.", "doi" : "10.1088/2632-2153/ae4442", "bibtexKey": "Nikolaou_2026" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2e0b01cb7a9056480740c346814c8e3f7/simtech", "tags" : [ "EXC2075","PN6","PN6-5(II)","curated" ], "intraHash" : "e0b01cb7a9056480740c346814c8e3f7", "interHash" : "3d9e0fd219e61bbd8c4713622fd0680e", "label" : "Efficient Confidence Interval Computation for Physics-Aware Machine Learning of Diffusion\u2013Sorption Models", "user" : "simtech", "description" : "", "date" : "2026-02-19 11:39:25", "changeDate" : "2026-04-13 14:57:11", "count" : 6, "pub-type": "article", "journal": "Frontiers in Water: Advances in Model-Data Fusion for Water Resources Problems", "year": "2026 (submitted)", "url": "", "author": [ "Stefania Scheurer","Riccardo Frenner","Tim Brünnette","Sergey Oladyshkin","Wolfgang Nowak" ], "authors": [ {"first" : "Stefania", "last" : "Scheurer"}, {"first" : "Riccardo", "last" : "Frenner"}, {"first" : "Tim", "last" : "Brünnette"}, {"first" : "Sergey", "last" : "Oladyshkin"}, {"first" : "Wolfgang", "last" : "Nowak"} ], "bibtexKey": "scheurer2026confidence" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2e90294c3b5b554eaf29e779847e41793/simtech", "tags" : [ "EXC2075","PN6","PN6A-4","curated" ], "intraHash" : "e90294c3b5b554eaf29e779847e41793", "interHash" : "3fcd475e81478032c4de1c5732f0589f", "label" : "When Physics Gets in the Way: An Entropy-based Evaluation of Conceptual Constraints in Hybrid Hydrological Models", "user" : "simtech", "description" : "", "date" : "2026-02-04 09:17:34", "changeDate" : "2026-02-16 15:06:37", "count" : 5, "pub-type": "article", "journal": "Hydrology and Earth System Sciences", "year": "2026", "url": "", "author": [ "Manuel Álvarez Chaves","Eduardo Acuña Espinoza","Uwe Ehret","Anneli Guthke" ], "authors": [ {"first" : "Manuel", "last" : "Álvarez Chaves"}, {"first" : "Eduardo", "last" : "Acuña Espinoza"}, {"first" : "Uwe", "last" : "Ehret"}, {"first" : "Anneli", "last" : "Guthke"} ], "volume": "30","number": "3","pages": "629--658", "doi" : "10.5194/hess-30-629-2026", "bibtexKey": "alvarezchaves2025physics" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/204ce87872250fefd91fcf4cdfd8965b9/simtech", "tags" : [ "EXC2075","PN6","PN6A-4","curated" ], "intraHash" : "04ce87872250fefd91fcf4cdfd8965b9", "interHash" : "5e72757dfa96fce2fd2a993c390212d3", "label" : "A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling", "user" : "simtech", "description" : "", "date" : "2026-01-20 12:22:07", "changeDate" : "2026-02-16 15:06:37", "count" : 5, "pub-type": "article", "journal": "Machine Learning: Earth", "year": "2026", "url": "", "author": [ "Manuel Álvarez Chaves","Eduardo Acuña Espinoza","Daniel Klotz","Hoshin Gupta","Uwe Ehret","Anneli Guthke" ], "authors": [ {"first" : "Manuel", "last" : "Álvarez Chaves"}, {"first" : "Eduardo", "last" : "Acuña Espinoza"}, {"first" : "Daniel", "last" : "Klotz"}, {"first" : "Hoshin", "last" : "Gupta"}, {"first" : "Uwe", "last" : "Ehret"}, {"first" : "Anneli", "last" : "Guthke"} ], "volume": "under review", "bibtexKey": "alvarezchaves2026variational" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2220c6d85e156e27f5708a9bb8bd43703/simtech", "tags" : [ "EXC2075","PN6","PN6-8(II)","curated" ], "intraHash" : "220c6d85e156e27f5708a9bb8bd43703", "interHash" : "de2965a166288b23d3e079c4ea3ff25d", "label" : "Voronoi Cell Interface\u2010Based Parameter Sensitivity Analysis for Labeled Samples", "user" : "simtech", "description" : "", "date" : "2026-01-19 11:05:41", "changeDate" : "2026-02-16 15:06:37", "count" : 7, "pub-type": "article", "journal": "Computer Graphics Forum","publisher":"Wiley", "year": "2025", "url": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.70122", "author": [ "Ruben Bauer","Marina Evers","Quynh Quang Ngo","Guido Reina","Steffen Frey","Michael Sedlmair" ], "authors": [ {"first" : "Ruben", "last" : "Bauer"}, {"first" : "Marina", "last" : "Evers"}, {"first" : "Quynh Quang", "last" : "Ngo"}, {"first" : "Guido", "last" : "Reina"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Michael", "last" : "Sedlmair"} ], "affiliation" : "Bauer, Ruben, Visualisierungsinstitut der Universität Stuttgart. Reina, Guido, Visualisierungsinstitut der Universität Stuttgart. Frey, Steffen, Visualisierungsinstitut der Universität Stuttgart. Sedlmair, Michael, Visualisierungsinstitut der Universität Stuttgart", "orcid-numbers" : "Reina, Guido/0000-0003-4127-1897, Frey, Steffen/0000-0002-1872-6905, Sedlmair, Michael/0000-0001-7048-9292", "doi" : "10.1111/cgf.70122", "bibtexKey": "bauer:2025:voronoi" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/271b1ce7bb5a0fc0421e93b0ff2035fb3/simtech", "tags" : [ "EXC2075","PN6","PN6-8(II)","curated" ], "intraHash" : "71b1ce7bb5a0fc0421e93b0ff2035fb3", "interHash" : "e3f3a16f66c0c0afd4a7343ab0657ab0", "label" : "QVis: Query-based Visual Analysis of Multiscale Patterns in Spatiotemporal Ensembles", "user" : "simtech", "description" : "", "date" : "2026-01-19 11:03:43", "changeDate" : "2026-02-16 15:06:37", "count" : 5, "pub-type": "article", "journal": "IEEE Transactions on Visualization and Computer Graphics","publisher":"Institute of Electrical and Electronics Engineers (IEEE)", "year": "2025", "url": "https://ieeexplore.ieee.org/document/11236970", "author": [ "Ruben Bauer","Quynh Quang Ngo","Guido Reina","Steffen Frey","Michael Sedlmair" ], "authors": [ {"first" : "Ruben", "last" : "Bauer"}, {"first" : "Quynh Quang", "last" : "Ngo"}, {"first" : "Guido", "last" : "Reina"}, {"first" : "Steffen", "last" : "Frey"}, {"first" : "Michael", "last" : "Sedlmair"} ], "pages": "1-15", "affiliation" : "Bauer, Ruben, Visualisierungsinstitut der Universität Stuttgart. Reina, Guido, Visualisierungsinstitut der Universität Stuttgart. Frey, Steffen, Visualisierungsinstitut der Universität Stuttgart. Sedlmair, Michael, Visualisierungsinstitut der Universität Stuttgart", "orcid-numbers" : "Reina, Guido/0000-0003-4127-1897, Frey, Steffen/0000-0002-1872-6905, Sedlmair, Michael/0000-0001-7048-9292", "doi" : "10.1109/tvcg.2025.3629575", "bibtexKey": "bauer:2025:qvis:" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/247d8d16c0daf02723d0d9adf3a44e9ff/simtech", "tags" : [ "EXC2075","PN6","PN6-14","curated" ], "intraHash" : "47d8d16c0daf02723d0d9adf3a44e9ff", "interHash" : "76f3c992f69111d33ec521b575931aac", "label" : "Deep Learning for Metabolic Rate Estimation from Biosignals: A Comparative Study of Architectures and Signal Selection", "user" : "simtech", "description" : "", "date" : "2025-12-18 11:23:11", "changeDate" : "2026-02-09 16:45:24", "count" : 4, "pub-type": "proceedings", "publisher":"BMVC conference, MPI workshop", "year": "2025", "url": "https://bmva-archive.org.uk/bmvc/2025/assets/workshops/MPI/Paper_1/paper.pdf", "author": [ "Alina Roitberg Sarvenaz Babakhani, David Remy" ], "authors": [ {"first" : "Alina Roitberg", "last" : "Sarvenaz Babakhani, David Remy"} ], "abstract": "Energy expenditure estimation aims to infer human metabolic rate from physiological signals such as heart rate, respiration, or accelerometer, and has been studied primarily with classical regression methods. The few existing deep learning approaches rarely disentangle the role of neural architecture from that of signal choice. In this work, we systematically evaluate both aspects. We compare classical baselines with newer neural architectures across single signals, signal pairs, and grouped sensor inputs for diverse physical activities. Our results show that minute ventilation is the most predictive individual signal, with a transformer model achieving the lowest root mean square error (RMSE) of 0.87W/kg across all activities. Paired and grouped signals, such as those from the Hexoskin smart shirt (5 signals), offer good alternatives for faster models like CNN and ResNet with attention. Per-activity evaluation revealed mixed outcomes: notably better outcomes in low-intensity activities (RMSE down to 0.29W/kg; NRMSE = 0.04), while higher-intensity tasks showed larger RMSE but more comparable normalized errors. Finally, subject-level analysis highlights strong inter-individual variability, motivating the need for adaptive modeling strategies. Our code and models will be publicly available at this GitHub repository: https://github.com/Sarvibabakhani/deeplearning-biosignals-ee", "language" : "english", "bibtexKey": "noauthororeditor" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/280aaae58435edc64c924107e9bdd13b6/simtech", "tags" : [ "EXC2075","PN6","PN6A-4","curated" ], "intraHash" : "80aaae58435edc64c924107e9bdd13b6", "interHash" : "d0e5e3e02abea718585057cc8aa41d17", "label" : "Comparing Physics-based, Conceptual and Machine-Learning Models to Predict Groundwater Levels by Bayesian Model Averaging", "user" : "simtech", "description" : "", "date" : "2025-12-04 17:10:31", "changeDate" : "2026-02-09 16:45:24", "count" : 5, "pub-type": "article", "journal": "Groundwater", "year": "2025", "url": "", "author": [ "Thomas Wöhling","A.O. Crespo Delgadillo","M. Kraft","Anneli Guthke" ], "authors": [ {"first" : "Thomas", "last" : "Wöhling"}, {"first" : "A.O.", "last" : "Crespo Delgadillo"}, {"first" : "M.", "last" : "Kraft"}, {"first" : "Anneli", "last" : "Guthke"} ], "volume": "63","pages": "484-505", "doi" : "10.1111/gwat.13487", "bibtexKey": "wohling2024comparing" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2ba825910c3848b4be0adc10b402f3200/simtech", "tags" : [ "EXC2075","PN3","PN3A-1","PN6","curated" ], "intraHash" : "ba825910c3848b4be0adc10b402f3200", "interHash" : "7655d709fe9281f8df736282e4be4965", "label" : "Collective variables of neural networks: empirical time evolution and scaling laws", "user" : "simtech", "description" : "", "date" : "2025-08-14 12:38:19", "changeDate" : "2026-02-16 15:06:37", "count" : 6, "pub-type": "article", "journal": "Machine Learning: Science and Technology","publisher":"IOP Publishing", "year": "2025", "url": "http://dx.doi.org/10.1088/2632-2153/adee76", "author": [ "Samuel Tovey","Sven Krippendorf","Michael Spannowsky","Konstantin Nikolaou","Christian Holm" ], "authors": [ {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Sven", "last" : "Krippendorf"}, {"first" : "Michael", "last" : "Spannowsky"}, {"first" : "Konstantin", "last" : "Nikolaou"}, {"first" : "Christian", "last" : "Holm"} ], "volume": "6","number": "3","pages": "035021", "issn" : "2632-2153", "doi" : "10.1088/2632-2153/adee76", "bibtexKey": "Tovey_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cf1995c9717639d402e4d18cedbbf5a0/simtech", "tags" : [ "EXC2075","PN6","curated" ], "intraHash" : "cf1995c9717639d402e4d18cedbbf5a0", "interHash" : "59c297d4ca5bae708a65866c3d88df79", "label" : "Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens", "user" : "simtech", "description" : "", "date" : "2025-07-14 08:57:45", "changeDate" : "2026-02-16 15:06:37", "count" : 5, "pub-type": "preprint", "year": "2025", "url": "https://arxiv.org/abs/2507.05035", "author": [ "Konstantin Nikolaou","Sven Krippendorf","Samuel Tovey","Christian Holm" ], "authors": [ {"first" : "Konstantin", "last" : "Nikolaou"}, {"first" : "Sven", "last" : "Krippendorf"}, {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Christian", "last" : "Holm"} ], "eprint" : "2507.05035", "archiveprefix" : "arXiv", "primaryclass" : "cs.LG", "bibtexKey": "nikolaou2025scalingcurvesinternaldynamics" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/20d0a260ffdf08f7d114fc508b8e2b33f/simtech", "tags" : [ "EXC2075","PN3","PN3-11","PN4","PN6","curated" ], "intraHash" : "0d0a260ffdf08f7d114fc508b8e2b33f", "interHash" : "0b1970f7c28aa561666ea785dfb94a98", "label" : "ART-SM: Boosting Fragment-Based Backmapping by Machine Learning", "user" : "simtech", "description" : "", "date" : "2025-05-29 10:53:24", "changeDate" : "2026-02-09 16:45:24", "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/2720ea127cbe3463fb14f67d3cdf8c335/simtech", "tags" : [ "EXC2075","PN6","curated" ], "intraHash" : "720ea127cbe3463fb14f67d3cdf8c335", "interHash" : "33ee06d7ccbedcf9bb5efbd6fa153446", "label" : "Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios", "user" : "simtech", "description" : "", "date" : "2025-05-12 11:29:41", "changeDate" : "2026-01-16 11:37:47", "count" : 11, "pub-type": "misc", "year": "2023", "url": "", "author": [ "Philipp Rodegast","Steffen Maier","Jonas Kneifl","Jörg Christoph Fehr" ], "authors": [ {"first" : "Philipp", "last" : "Rodegast"}, {"first" : "Steffen", "last" : "Maier"}, {"first" : "Jonas", "last" : "Kneifl"}, {"first" : "Jörg Christoph", "last" : "Fehr"} ], "note": "Related to: Rodegast, P., Maier, S., Kneifl, J., Fehr, J.: On using Machine Learning Algorithms for Motorcycle Collision Detection, 2023. tbd","abstract": "This dataset provides time-dependent simulation results from high-fidelity motorcycle body crash scenarios. The set contains the angular as well as linear positions, velocities, and accelerations of different parts of the motorcycle. In addition, force and contact sensor signals are also part of the dataset. The driving scenarios include critical, i.e., crash scenarios, as well as non-critical ones. They simulations result from a parametrized scenario description and from scenarios which follow ISO 13232.Content Time trajectories of sensor signals for operational and crash scenarios (*.csv files): time-dependent sensor measurements resulting from a variety of simulated scenarios  TrainingData.csv (~40.000 Samples): Scenarios used for training TestData.csv (~9000 Samples): Scenarios used for testing ControlScenarios (including 39 .csv files): Scenarios used for validation (including ISO 13232 scenarios) Script to load the data with data description (LoadData.py)", "affiliation" : "Rodegast, Philipp/ISG Industrielle Steuerungstechnik GmbH, Maier, Steffen/University of Stuttgart, Kneifl, Jonas/University of Stuttgart, Fehr, Jörg/University of Stuttgart", "orcid-numbers" : "Rodegast, Philipp/0000-0002-9794-7852, Maier, Steffen/0000-0003-4569-722X, Kneifl, Jonas/0000-0003-3934-6968, Fehr, Jörg/0000-0003-2850-1440", "doi" : "10.18419/darus-3301", "bibtexKey": "rodegast2023simulation" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/23be6904a3bdc2a976ff383c3567d75c3/simtech", "tags" : [ "PN6-2(II)","curated","PN6" ], "intraHash" : "3be6904a3bdc2a976ff383c3567d75c3", "interHash" : "864cd77e207a666bb9f86864b9b5d454", "label" : "Short Paper: Evaluation of stdpar Compilers on a Kernel Matrix Assembly and a BLAS Level 3 SYMM Kernel", "user" : "simtech", "description" : "", "date" : "2024-12-27 08:04:37", "changeDate" : "2025-06-23 09:47:05", "count" : 9, "pub-type": "inproceedings", "journal": "2024 23rd International Symposium on Parallel and Distributed Computing (ISPDC)", "year": "2024", "url": "http://dx.doi.org/10.1109/ispdc62236.2024.10705404", "author": [ "Marcel Breyer","Alexander Van Craen","Dirk Pflüger" ], "authors": [ {"first" : "Marcel", "last" : "Breyer"}, {"first" : "Alexander Van", "last" : "Craen"}, {"first" : "Dirk", "last" : "Pflüger"} ], "editor": [ "Marcel Breyer","Alexander Van Craen","Dirk Pflüger" ], "editors": [ {"first" : "Marcel", "last" : "Breyer"}, {"first" : "Alexander", "last" : "Van Craen"}, {"first" : "Dirk", "last" : "Pflüger"} ], "doi" : "10.1109/ispdc62236.2024.10705404", "bibtexKey": "breyer2024short" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2a935bae2b08351be41efd97fae95465e/simtech", "tags" : [ "EXC2075","PN6","PN6-5","PN6-5(II)","curated" ], "intraHash" : "a935bae2b08351be41efd97fae95465e", "interHash" : "ae2771c0b5955a966fc1cef34a99462e", "label" : "Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy", "user" : "simtech", "description" : "", "date" : "2024-12-09 17:32:12", "changeDate" : "2025-01-27 13:14:14", "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/20d96128b61e1e64fe07a0889834cf99c/simtech", "tags" : [ "EXC2075","PN6","PN6-5","PN6-5(II)","curated" ], "intraHash" : "0d96128b61e1e64fe07a0889834cf99c", "interHash" : "23bb54fcd793f1ff09321766c81ad8c0", "label" : "Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking", "user" : "simtech", "description" : "", "date" : "2024-12-09 17:31:04", "changeDate" : "2025-01-27 13:14:14", "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/2aeeb14efd7abd3f25bd7cb1c334b9b02/simtech", "tags" : [ "EXC2075","PN3","PN3A-8","PN5","PN5-7","PN6","PN6-1(II)","curated" ], "intraHash" : "aeeb14efd7abd3f25bd7cb1c334b9b02", "interHash" : "4bb75fc30869377dbea5eaa528b58e4f", "label" : "Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems", "user" : "simtech", "description" : "", "date" : "2024-10-15 17:32:31", "changeDate" : "2025-01-27 13:14:14", "count" : 5, "pub-type": "misc", "year": "2024", "url": "https://arxiv.org/abs/2405.10465", "author": [ "Robin Herkert","Patrick Buchfink","Bernard Haasdonk","Johannes Rettberg","Jörg 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/21eb2bb4951a0104ba400e5f9529ce502/simtech", "tags" : [ "EXC2075","PN3","PN3A-8","PN5","PN5-7","PN6","PN6-1(II)","curated" ], "intraHash" : "1eb2bb4951a0104ba400e5f9529ce502", "interHash" : "e9d1b612a6b17d90226f0f74c57a399e", "label" : "Randomized Symplectic Model Order Reduction for Hamiltonian Systems", "user" : "simtech", "description" : "", "date" : "2024-10-15 17:23:57", "changeDate" : "2025-01-27 13:14:14", "count" : 4, "pub-type": "inproceedings", "booktitle": "Large-Scale Scientific Computations","publisher":"Springer Nature Switzerland","address":"Cham", "year": "2024", "url": "", "author": [ "R. 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/2884f529f6a889cf4e38b853501114fde/simtech", "tags" : [ "EXC2075","PN6","PN6-2","curated" ], "intraHash" : "884f529f6a889cf4e38b853501114fde", "interHash" : "9b7ef067104bc4e2fe485fbeae5721a3", "label" : "A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps", "user" : "simtech", "description" : "", "date" : "2024-09-30 18:20:39", "changeDate" : "2025-01-27 13:14:14", "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/22b58ea3f5c19661e59e9fe0e6cac098d/simtech", "tags" : [ "EXC2075","PN6","PN6-2","curated" ], "intraHash" : "2b58ea3f5c19661e59e9fe0e6cac098d", "interHash" : "b673577efe95c0d908359b2178b8b64d", "label" : "PDEBench Pretrained Models : Pretrained models for \"PDEBench: An Extensive Benchmark for Scientific Machine Learning\"", "user" : "simtech", "description" : "", "date" : "2024-09-30 18:20:11", "changeDate" : "2025-01-27 13:14:14", "count" : 7, "pub-type": "misc", "year": "2022", "url": "", "author": [ "Makoto Takamoto","Timothy Praditia","Raphael Leiteritz","Dan MacKinlay","Francesco Alesiani","Dirk Pflüger","Mathias Niepert" ], "authors": [ {"first" : "Makoto", "last" : "Takamoto"}, {"first" : "Timothy", "last" : "Praditia"}, {"first" : "Raphael", "last" : "Leiteritz"}, {"first" : "Dan", "last" : "MacKinlay"}, {"first" : "Francesco", "last" : "Alesiani"}, {"first" : "Dirk", "last" : "Pflüger"}, {"first" : "Mathias", "last" : "Niepert"} ], "note": "Related to: Takamoto, M., Praditia, T., Leiteritz, R., MacKinlay, D., Alesiani, F., Pflüger, D. and Niepert, M.: PDEBench: An Extensive Benchmark for Scientific Machine Learning. submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks","abstract": "This dataset contains the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. In addition the dataset contains the pre-trained model for the 1D Inverse problem for FNO and U-Net. These models are stored using the same structure as the dataset they trained on. All the files are saved in .pt files, the default file type for the PyTorch library.More detailed information are also provided in our Github repository (https://github.com/pdebench/PDEBench) and our submitting paper to NeurIPS 2022 Benchmark track.", "affiliation" : "Takamoto, Makoto/NEC Labs Europe, Praditia, Timothy/Universität Stuttgart, Leiteritz, Raphael/Universität Stuttgart, MacKinlay, Dan/CSIRO's Data61, Alesiani, Francesco/NEC Labs Europe, Pflüger, Dirk/Universität Stuttgart, Niepert, Mathias/Universität Stuttgart", "orcid-numbers" : "Takamoto, Makoto/0000-0001-7192-1956, Praditia, Timothy/0000-0003-3619-9122, Leiteritz, Raphael/0000-0001-8070-2384, MacKinlay, Dan/0000-0001-6077-2684, Alesiani, Francesco/0000-0003-4413-7247, Pflüger, Dirk/0000-0002-4360-0212, Niepert, Mathias/0000-0002-8401-3751", "doi" : "10.18419/darus-2987", "bibtexKey": "takamoto2022pdebench" } ] }