{"d8337fef78da9afa5e09d133f3ddaadctestusersimtech":{"DOI":"","ISBN":"","ISSN":"","URL":"https://doi.org/10.1016/j.matcom.2024.07.036","abstract":"","annote":"","author":[{"family":"Barth","given":"Andrea"},{"family":"Stein","given":"Andreas"}],"citation-label":"journals/corr/abs-1910-14657","collection-editor":[],"collection-title":"","container-author":[],"container-title":"Mathematics and Computers in Simulation","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2025"]],"literal":"2025"},"event-place":"","id":"d8337fef78da9afa5e09d133f3ddaadctestusersimtech","interhash":"10a468b05935bc09e64db5be6dd8b6ac","intrahash":"d8337fef78da9afa5e09d133f3ddaadc","issue":"","issued":{"date-parts":[["2025"]],"literal":"2025"},"keyword":"EXC2075 PN5 PN5-11 selected","misc":{"ee":"http://arxiv.org/abs/1910.14657"},"note":"","number":"","number-of-pages":"23","page":"347-370","page-first":"347","publisher":"","publisher-place":"","status":"","title":"A stochastic transport problem with Lévy noise: Fully discrete numerical approximation.","type":"article-journal","username":"testusersimtech","version":"","volume":"227"},"65200d7a79f2d53cbf585dc001367089testusersimtech":{"DOI":"10.3847/1538-4357/ad6f9a","ISBN":"","ISSN":"","URL":"http://dx.doi.org/10.3847/1538-4357/ad6f9a","abstract":"","annote":"","author":[{"family":"del Valle","given":"Juan Carlos"},{"family":"Redondo","given":"Pilar"},{"family":"Kästner","given":"Johannes"},{"family":"Molpeceres","given":"Germán"}],"citation-label":"val24","collection-editor":[],"collection-title":"","container-author":[],"container-title":"Astrophys. 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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 $8.9e-3$.","annote":"","author":[{"family":"Leiteritz","given":"Raphael"},{"family":"Hurler","given":"Marcel"},{"family":"Pflüger","given":"Dirk"}],"citation-label":"leiteritz2021learning","collection-editor":[],"collection-title":"","container-author":[],"container-title":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2021"]],"literal":"2021"},"event-place":"","id":"baadf322b0b79fe1bc38454c418b0375testusersimtech","interhash":"a3ae90dbf6cd17e3488488dffffd0934","intrahash":"baadf322b0b79fe1bc38454c418b0375","issue":"","issued":{"date-parts":[["2021"]],"literal":"2021"},"keyword":"EXC2075 PN6 PN6-2 selected","misc":{"isbn":"978-1-6654-4337-1","doi":"10.1109/ICMLA52953.2021.00266"},"note":"","number":"","number-of-pages":"5","page":"1664-1669","page-first":"1664","publisher":"","publisher-place":"","status":"","title":"Learning Free-Surface Flow with Physics-Informed Neural Networks","type":"paper-conference","username":"testusersimtech","version":"","volume":""},"cd9efcbe6c558e7c9d560a12c0abbb48testusersimtech":{"DOI":"10.1021/acs.jpcb.4c04456","ISBN":"","ISSN":"1520-5207","URL":"http://dx.doi.org/10.1021/acs.jpcb.4c04456","abstract":"","annote":"","author":[{"family":"Fleck","given":"Maximilian"},{"family":"Darouich","given":"Samir"},{"family":"Hansen","given":"Niels"},{"family":"Gross","given":"Joachim"}],"citation-label":"Fleck_2024","collection-editor":[],"collection-title":"","container-author":[],"container-title":"The Journal of Physical Chemistry B","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2024","09"]],"literal":"2024"},"event-place":"","id":"cd9efcbe6c558e7c9d560a12c0abbb48testusersimtech","interhash":"6d3fafd4d984eea4315ffa87b2a19661","intrahash":"cd9efcbe6c558e7c9d560a12c0abbb48","issue":"39","issued":{"date-parts":[["2024","09"]],"literal":"2024"},"keyword":"EXC2075 PN3-8 PN3-8(II) selected","misc":{"issn":"1520-5207","doi":"10.1021/acs.jpcb.4c04456"},"note":"","number":"39","number-of-pages":"8","page":"9544–9552","page-first":"9544","publisher":"American Chemical Society (ACS)","publisher-place":"","status":"","title":"TAMie Force Field for Alkanethiols: Multifidelity Gaussian Processes for Dealing with Scarce Experimental Data","type":"article-journal","username":"testusersimtech","version":"","volume":"128"},"5944e92567502f815dd268d2a8973b21testusersimtech":{"DOI":"10.18653/v1/2023.conll-1.24","ISBN":"","ISSN":"","URL":"https://aclanthology.org/2023.conll-1.24","abstract":"Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image--text pairs, models fail to show fine-grained understanding of the combined semantics of these modalities. To this end, we propose Hard Negative Captions (HNC): an automatically created dataset containing foiled hard negative captions for ITM training towards achieving fine-grained cross-modal comprehension in VL. Additionally, we provide a challenging manually-created test set for benchmarking models on a fine-grained cross-modal mismatch with varying levels of compositional complexity. Our results show the effectiveness of training on HNC by improving the models' zero-shot capabilities in detecting mismatches on diagnostic tasks and performing robustly under noisy visual input scenarios. Also, we demonstrate that HNC models yield a comparable or better initialization for fine-tuning. Our code and data are publicly available.","annote":"","author":[{"family":"Dönmez","given":"Esra"},{"family":"Tilli","given":"Pascal"},{"family":"Yang","given":"Hsiu-Yu"},{"family":"Vu","given":"Ngoc Thang"},{"family":"Silberer","given":"Carina"}],"citation-label":"donmez-etal-2023-hnc","collection-editor":[{"family":"Jiang","given":"Jing"},{"family":"Reitter","given":"David"},{"family":"Deng","given":"Shumin"}],"collection-title":"","container-author":[{"family":"Jiang","given":"Jing"},{"family":"Reitter","given":"David"},{"family":"Deng","given":"Shumin"}],"container-title":"Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)","documents":[],"edition":"","editor":[{"family":"Jiang","given":"Jing"},{"family":"Reitter","given":"David"},{"family":"Deng","given":"Shumin"}],"event-date":{"date-parts":[["2023","12"]],"literal":"2023"},"event-place":"Singapore","id":"5944e92567502f815dd268d2a8973b21testusersimtech","interhash":"c8bde8198bf201783db70355cc494958","intrahash":"5944e92567502f815dd268d2a8973b21","issue":"","issued":{"date-parts":[["2023","12"]],"literal":"2023"},"keyword":"EXC2075 PN6-5 PN6-5(II) selected","misc":{"doi":"10.18653/v1/2023.conll-1.24"},"note":"","number":"","number-of-pages":"24","page":"364--388","page-first":"364","publisher":"Association for Computational Linguistics","publisher-place":"Singapore","status":"","title":"HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities","type":"paper-conference","username":"testusersimtech","version":"","volume":""},"112b47e516f3b08e53d295d89027127atestusersimtech":{"DOI":"10.1038/s41598-024-75795-2","ISBN":"","ISSN":"2045-2322","URL":"https://doi.org/10.1038/s41598-024-75795-2","abstract":"During muscle contraction, not only are the fascicles shortening but also the pennation angle changes, which leads to a faster contraction of the muscle than of its fascicles. This phenomenon is called muscle gearing, and it has a direct influence on the force output of the muscle. There are few studies showing pennation angle changes during isometric and concentric contractions for different contraction intensities and muscle lengths. Therefore, the aim was to determine these influences over a wide range of contraction intensities and ankle joint angles for human triceps surae. Additionally, the influence of contraction intensity and ankle joint angle on muscle gearing was evaluated. Ten sport students performed concentric and isometric contractions with intensities between 0 and 90\\% of the maximum voluntary contraction and ankle joint angles from 50° to 120°. During these contractions, the m. gastrocnemius medialis and lateralis and the m. soleus were recorded via ultrasound imaging. A nonlinear relationship between fascicle length and pennation angle was discovered, which can be described with a quadratic fit for each of the muscles during isometric contraction. A nearly identical relationship was detected during dynamic contraction. The muscle gearing increased almost linearly with contraction intensity and ankle joint angle.","annote":"","author":[{"family":"Coenning","given":"Corinna"},{"family":"Rieg","given":"Volker"},{"family":"Siebert","given":"Tobias"},{"family":"Wank","given":"Veit"}],"citation-label":"Coenning2024","collection-editor":[{"family":"Siebert","given":"Tobias"}],"collection-title":"","container-author":[{"family":"Siebert","given":"Tobias"}],"container-title":"Scientific Reports","documents":[],"edition":"","editor":[{"family":"Siebert","given":"Tobias"}],"event-date":{"date-parts":[["2024","10","22"]],"literal":"2024"},"event-place":"","id":"112b47e516f3b08e53d295d89027127atestusersimtech","interhash":"7118ef30d9e0d0109338f9b611d56769","intrahash":"112b47e516f3b08e53d295d89027127a","issue":"1","issued":{"date-parts":[["2024","10","22"]],"literal":"2024"},"keyword":"EXC2075 PN2 PN2-8Siebert selected","misc":{"issn":"2045-2322","doi":"10.1038/s41598-024-75795-2"},"note":"","number":"1","page":"24929","page-first":"24929","publisher":"","publisher-place":"","status":"","title":"Impact of contraction intensity and ankle joint angle on calf muscle fascicle length and pennation angle during isometric and dynamic contractions","type":"article-journal","username":"testusersimtech","version":"","volume":"14"},"06617cdd3b123aca9801eb5205b09f1ftestusersimtech":{"DOI":"10.1007/s00466-024-02553-6","ISBN":"","ISSN":"1432-0924","URL":"https://doi.org/10.1007/s00466-024-02553-6","abstract":"Highly nonlinear dynamic finite element simulations using explicit time integration are particularly valuable tools for structural analysis in fields like automotive, aerospace, and civil engineering, or in the study of injury biomechanics. However, such state-of-the-art simulation models demand significant computational resources. Conventional data-driven surrogate modeling approaches address this by evolving the dynamics on low-dimensional embeddings, yet the majority of them operate directly on high-resolution data obtained from numerical discretizations, making them costly and unsuitable for adaptive resolutions or for handling information flow over large spatial distances. We therefore propose a multi-hierarchical framework for the structured creation of a series of surrogate models at different resolutions. Macroscale features are captured on coarse surrogates, while microscale effects are resolved on finer ones, while leveraging transfer learning to pass information between scales. The objective of this study is to develop efficient surrogates for a kart frame model in a frontal impact scenario. To achieve this, its mesh is simplified to obtain multi-resolution representations of the kart. Subsequently, a graph-convolutional neural network-based surrogate learns parameter-dependent low-dimensional latent dynamics on the coarsest representation. Following surrogates are trained on residuals using finer resolutions, allowing for multiple surrogates with varying hardware requirements and increasing accuracy.","annote":"","author":[{"family":"Kneifl","given":"Jonas"},{"family":"Fehr","given":"Jörg"},{"family":"Brunton","given":"Steven L."},{"family":"Kutz","given":"J. Nathan"}],"citation-label":"Kneifl2024","collection-editor":[],"collection-title":"","container-author":[],"container-title":"Computational Mechanics","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2024","10","05"]],"literal":"2024"},"event-place":"","id":"06617cdd3b123aca9801eb5205b09f1ftestusersimtech","interhash":"2ffaae3d8e2209369441c9740b61edf8","intrahash":"06617cdd3b123aca9801eb5205b09f1f","issue":"","issued":{"date-parts":[["2024","10","05"]],"literal":"2024"},"keyword":"EXC2075 PN7 PN7-6(II) selected","misc":{"issn":"1432-0924","preprinturl":"https://doi.org/10.48550/arXiv.2402.09234","doi":"10.1007/s00466-024-02553-6"},"note":"","number":"","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks","type":"article-journal","username":"testusersimtech","version":"","volume":""},"9f29154735b58797b7bdcb76a162cbcftestusersimtech":{"DOI":"10.1063/5.0037938","ISBN":"","ISSN":"","URL":"https://doi.org/10.1063%2F5.0037938","abstract":"","annote":"","author":[{"family":"Carral","given":"Ángel D\\'ıaz"},{"family":"Ostertag","given":"Magnus"},{"family":"Fyta","given":"Maria"}],"citation-label":"D_az_Carral_2021","collection-editor":[],"collection-title":"","container-author":[],"container-title":"The Journal of Chemical Physics","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2021","01"]],"literal":"2021"},"event-place":"","id":"9f29154735b58797b7bdcb76a162cbcftestusersimtech","interhash":"6e379372559086387b40dde24215cb39","intrahash":"9f29154735b58797b7bdcb76a162cbcf","issue":"4","issued":{"date-parts":[["2021","01"]],"literal":"2021"},"keyword":"EXC2075 PN3 PN3-7 selected","misc":{"doi":"10.1063/5.0037938"},"note":"","number":"4","page":"044111","page-first":"044111","publisher":"AIP Publishing","publisher-place":"","status":"","title":"Deep learning for nanopore ionic current blockades","type":"article-journal","username":"testusersimtech","version":"","volume":"154"},"74ebb87478e426fb1ca3ed8d21b1db0dtestusersimtech":{"DOI":"","ISBN":"","ISSN":"","URL":"https://aclanthology.org/2024.lrec-main.806","abstract":"The large success of deep learning based methods in Visual Question Answering (VQA) has concurrently increased the demand for explainable methods. Most methods in Explainable Artificial Intelligence (XAI) focus on generating post-hoc explanations rather than taking an intrinsic approach, the latter characterizing an interpretable model. In this work, we introduce an interpretable approach for graph-based VQA and demonstrate competitive performance on the GQA dataset. This approach bridges the gap between interpretability and performance. Our model is designed to intrinsically produce a subgraph during the question-answering process as its explanation, providing insight into the decision making. To evaluate the quality of these generated subgraphs, we compare them against established post-hoc explainability methods for graph neural networks, and perform a human evaluation. Moreover, we present quantitative metrics that correlate with the evaluations of human assessors, acting as automatic metrics for the generated explanatory subgraphs. Our code will be made publicly available at link removed due to anonymity period.","annote":"","author":[{"family":"Tilli","given":"Pascal"},{"family":"Vu","given":"Ngoc Thang"}],"citation-label":"tilli-vu-2024-intrinsic","collection-editor":[{"family":"Calzolari","given":"Nicoletta"},{"family":"Kan","given":"Min-Yen"},{"family":"Hoste","given":"Veronique"},{"family":"Lenci","given":"Alessandro"},{"family":"Sakti","given":"Sakriani"},{"family":"Xue","given":"Nianwen"}],"collection-title":"","container-author":[{"family":"Calzolari","given":"Nicoletta"},{"family":"Kan","given":"Min-Yen"},{"family":"Hoste","given":"Veronique"},{"family":"Lenci","given":"Alessandro"},{"family":"Sakti","given":"Sakriani"},{"family":"Xue","given":"Nianwen"}],"container-title":"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)","documents":[],"edition":"","editor":[{"family":"Calzolari","given":"Nicoletta"},{"family":"Kan","given":"Min-Yen"},{"family":"Hoste","given":"Veronique"},{"family":"Lenci","given":"Alessandro"},{"family":"Sakti","given":"Sakriani"},{"family":"Xue","given":"Nianwen"}],"event-date":{"date-parts":[["2024","05"]],"literal":"2024"},"event-place":"Torino, Italia","id":"74ebb87478e426fb1ca3ed8d21b1db0dtestusersimtech","interhash":"924c42b00a0554ad9e2adc79ed955ad7","intrahash":"74ebb87478e426fb1ca3ed8d21b1db0d","issue":"","issued":{"date-parts":[["2024","05"]],"literal":"2024"},"keyword":"EXC2075 PN6-5 PN6-5(II) selected","note":"","number":"","number-of-pages":"19","page":"9204--9223","page-first":"9204","publisher":"ELRA and ICCL","publisher-place":"Torino, Italia","status":"","title":"Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering","type":"paper-conference","username":"testusersimtech","version":"","volume":""},"8010c05fb55b9efeb6025dc2b5dc6212testusersimtech":{"DOI":"10.5194/hess-2024-60","ISBN":"","ISSN":"","URL":"","abstract":"","annote":"","author":[{"family":"Xu","given":"Teng"},{"family":"Xiao","given":"Sinan"},{"family":"Reuschen","given":"Sebastian"},{"family":"Wildt","given":"Nils"},{"family":"Franssen","given":"Harrie-Jan Hendricks"},{"family":"Nowak","given":"Wolfgang"}],"citation-label":"xu2024towards","collection-editor":[],"collection-title":"","container-author":[],"container-title":"Hydrology and Earth System Sciences","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2024 (accepted)"]],"literal":"2024 (accepted)"},"event-place":"","id":"8010c05fb55b9efeb6025dc2b5dc6212testusersimtech","interhash":"761ff9054529b7eb6f53550c26f605e4","intrahash":"8010c05fb55b9efeb6025dc2b5dc6212","issue":"","issued":{"date-parts":[["2024 (accepted)"]],"literal":"2024 (accepted)"},"keyword":"EXC2075 PN5 selected","misc":{"doi":"10.5194/hess-2024-60"},"note":"","number":"","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison","type":"article-journal","username":"testusersimtech","version":"","volume":""},"d645e1d39e2fd40bab22b53e22a73771testusersimtech":{"DOI":"10.1145/3615522.3615547","ISBN":"9798400707513","ISSN":"","URL":"https://doi.org/10.1145/3615522.3615547","abstract":"Scene-graph-based Visual Question Answering (VQA) has emerged as a burgeoning field in Deep Learning research, with a growing demand for robust and interpretable VQA systems. In this paper, we present a novel visual analysis approach that addresses two critical objectives in VQA: identifying and correcting prediction issues and providing insights into model decision-making processes through visualizing internal information. Our approach builds on the GraphVQA framework, which uses graph neural networks to process scene graphs representing images and which was trained on the widely-used GQA dataset. Our analysis tool aims at users familiar with the basics of graph-based VQA. By leveraging query-based scene analysis and visualization of crucial internal states, we are able to detect and pinpoint reasons for inaccurate predictions, facilitating model refinement and dataset curation. Identifying expressive internal states is a challenge. Through rigorous computer-based evaluations and presentation of a use case, we demonstrate the effectiveness of our analysis tool and model state visualization.","annote":"","author":[{"family":"Schäfer","given":"Noel"},{"family":"Künzel","given":"Sebastian"},{"family":"Munz-Körner","given":"Tanja"},{"family":"Tilli","given":"Pascal"},{"family":"Vidyapu","given":"Sandeep"},{"family":"Thang Vu","given":"Ngoc"},{"family":"Weiskopf","given":"Daniel"}],"citation-label":"10.1145/3615522.3615547","collection-editor":[],"collection-title":"VINCI '23","container-author":[],"container-title":"Proceedings of the 16th International Symposium on Visual Information Communication and Interaction","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2023"]],"literal":"2023"},"event-place":"Guangzhou, China","id":"d645e1d39e2fd40bab22b53e22a73771testusersimtech","interhash":"339c20d6111bec800cfc2c8f23acfcf9","intrahash":"d645e1d39e2fd40bab22b53e22a73771","issue":"","issued":{"date-parts":[["2023"]],"literal":"2023"},"keyword":"EXC2075 PN6-5 PN6-5(II) selected","misc":{"isbn":"9798400707513","numpages":"8","articleno":"25","location":"Guangzhou, China","doi":"10.1145/3615522.3615547"},"note":"","number":"","page":"","page-first":"","publisher":"Association for Computing Machinery","publisher-place":"Guangzhou, China","status":"","title":"Visual Analysis of Scene-Graph-Based Visual Question Answering","type":"paper-conference","username":"testusersimtech","version":"","volume":""},"c95bd672eec94118623f036030864e68testusersimtech":{"DOI":"10.1007/s10439-022-03087-9","ISBN":"","ISSN":"","URL":"https://link.springer.com/article/10.1007/s10439-022-03087-9","abstract":"Occupants exposed to low or moderate crash events can already suffer from whiplash-associated disorders leading to severe and long-lasting symptoms. However, the underlying injury mechanisms and the role of muscle activity are not fully clear. Potential increases in injury risk of non-nominal postures, i.e., rotated head, cannot be evaluated in detail due to the lack of experimental data. Examining changes in neck muscle activity to hold and stabilize the head in a rotated position during pre-crash scenarios might provide a deeper understanding of muscle reflex contributions and injury mechanisms. In this study, the influence of two different head postures (nominal vs. rotation of the head by about 63 ± 9° to the right) on neck muscle activity and head kinematics was investigated in simulated braking experiments inside a driving simulator. The braking scenario was implemented by visualization of the virtual scene using head-mounted displays and a combined translational-rotational platform motion. Kinematics of seventeen healthy subjects was tracked using 3D motion capturing. Surface electromyography were used to quantify muscle activity of left and right sternocleidomastoideus (SCM) and trapezius (TRP) muscles. The results show clear evidence that rotated head postures affect the static as well as the dynamic behavior of muscle activity during the virtual braking event. With head turned to the right, the contralateral left muscles yielded higher base activation and delayed muscle onset times. In contrast, right muscles had much lower activations and showed no relevant changes in muscle activation between nominal and rotated head position. The observed delayed muscle onset times and increased asymmetrical muscle activation patterns in the rotated head position are assumed to affect injury mechanisms. This could explain the prevalence of rotated head postures during a crash reported by patients suffering from WAD. The results can be used for validating the active behavior of human body models in braking simulations with nominal and rotated head postures, and to gain a deeper understanding of neck injury mechanisms.","annote":"","author":[{"family":"Kempter","given":"Fabian"},{"family":"Lantella","given":"Lorena"},{"family":"Stutzig","given":"Norman"},{"family":"Fehr","given":"Jörg"},{"family":"Siebert","given":"Tobias"}],"citation-label":"noauthororeditor2024rotated","collection-editor":[],"collection-title":"","container-author":[],"container-title":"Annals of Biomedical Engineering","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2024","04"]],"literal":"2024"},"event-place":"","id":"c95bd672eec94118623f036030864e68testusersimtech","interhash":"958da81d5faeafe01668d01f4a237c19","intrahash":"c95bd672eec94118623f036030864e68","issue":"","issued":{"date-parts":[["2024","04"]],"literal":"2024"},"keyword":"EXC2075 PN2 selected","misc":{"doi":"10.1007/s10439-022-03087-9"},"note":"","number":"","number-of-pages":"11","page":"771–782","page-first":"771","publisher":"","publisher-place":"","status":"","title":"Role of Rotated Head Postures on Volunteer Kinematics and Muscle Activity in Braking Scenarios Performed on a Driving Simulator","type":"article-journal","username":"testusersimtech","version":"","volume":"51"},"d967334e0e1673115b49cc5f64a3b7a6testusersimtech":{"DOI":"https://doi.org/10.1098/rsos.240776","ISBN":"","ISSN":"2054-5703","URL":"","abstract":"","annote":"","author":[{"family":"Hermann","given":"Sibylle"},{"family":"Fehr","given":"Jörg"}],"citation-label":"hermann2024accepteddocumenting","collection-editor":[],"collection-title":"","container-author":[],"container-title":"Royal Society Open Science","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2024","10"]],"literal":"2024"},"event-place":"","id":"d967334e0e1673115b49cc5f64a3b7a6testusersimtech","interhash":"567912cd4c1492f96e17d17d42aa3dce","intrahash":"d967334e0e1673115b49cc5f64a3b7a6","issue":"10","issued":{"date-parts":[["2024","10"]],"literal":"2024"},"keyword":"EXC2075 PNX selected","misc":{"issn":"2054-5703","doi":"https://doi.org/10.1098/rsos.240776"},"note":"","number":"10","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"Documenting Research in Simulation Science to Enhance Understanding for Reusability","type":"article-journal","username":"testusersimtech","version":"","volume":"11"},"c5b7ffca22c00c92de6a12568a6dbf70testusersimtech":{"DOI":"","ISBN":"","ISSN":"","URL":"","abstract":"","annote":"","author":[{"family":"Carral","given":"Angel Diaz"},{"family":"Sarap","given":"Chandra Shekar"},{"family":"Liu","given":"Ke"},{"family":"Radenovic","given":"Aleksandra"},{"family":"Fyta","given":"Maria"}],"citation-label":"carral20192d","collection-editor":[],"collection-title":"","container-author":[],"container-title":"2D Materials","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2019"]],"literal":"2019"},"event-place":"","id":"c5b7ffca22c00c92de6a12568a6dbf70testusersimtech","interhash":"3583022e8f2aa2051a5c7cbbbb2e29f1","intrahash":"c5b7ffca22c00c92de6a12568a6dbf70","issue":"4","issued":{"date-parts":[["2019"]],"literal":"2019"},"keyword":"EXC2075 PN3 PN3-7 selected","note":"","number":"4","page":"045011","page-first":"045011","publisher":"IOP Publishing","publisher-place":"","status":"","title":"2D MoS2 nanopores: ionic current blockade height for clustering DNA events","type":"article-journal","username":"testusersimtech","version":"","volume":"6"},"340c8a706e271ca8c9ab0e69e9834ea0testusersimtech":{"DOI":"10.1007/s10444-024-10195-8","ISBN":"","ISSN":"1572-9044","URL":"https://doi.org/10.1007/s10444-024-10195-8","abstract":"Projection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error can be bounded by residual-based methods, which are typically known to be highly pessimistic in the sense of largely overestimating the true error. This work applies two improved error bounding techniques, namely (a) a hierarchical error bound and (b) an error bound based on an auxiliary linear problem, to the case of port-Hamiltonian systems. The approaches rely on a secondary approximation of (a) the dynamical system and (b) the error system. In this paper, these methods are adapted to port-Hamiltonian systems. The mathematical relationship between the two methods is discussed both theoretically and numerically. 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