[{"status":"public","abstract":[{"lang":"eng","text":"Rubber-metal bushings (RMB) are critical components in multi-body systems, such as vehicles and industrial machinery, due to their ability to enable relative motion, dampen vibrations, and transmit forces. However, their nonlinear behavior challenges accurate modeling. Traditional physics-based models often fail to balance simplicity, accuracy, and computational efficiency. The growing availability of experimental data offers opportunities to improve RMB modeling through hybrid and data-driven approaches. This study evaluates physics-based, hybrid, and data-driven methods based on predictive accuracy, modeling effort, and computational cost. Hybrid approaches, combining machine learning techniques with physics-based models, are investigated to leverage their complementary strengths. Results show that hybrid methods enhance accuracy for simpler models with a modest increase in computational time. This highlights their potential to simplify RMB modeling while balancing accuracy and efficiency, offering insights for advancing multi-body system simulations. Building on these insights, data-driven methods are explored for their ability to provide surrogate models for dynamical systems without requiring expert knowledge. Experiments reveal that while simple data-driven methods approximate system behavior when data has low variance, they fail with trajectories of widely varying frequency and amplitude."}],"type":"journal_article","publication":"Multibody System Dynamics","language":[{"iso":"eng"}],"user_id":"43991","department":[{"_id":"151"}],"_id":"63765","citation":{"chicago":"Wohlleben, Meike Claudia, Jan Schütte, Manuel Bastian Berkemeier, Walter Sextro, and Sebastian Peitz. “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings.” <i>Multibody System Dynamics</i>, 2026, 1–21. <a href=\"https://doi.org/10.1007/s11044-026-10146-9\">https://doi.org/10.1007/s11044-026-10146-9</a>.","ieee":"M. C. Wohlleben, J. Schütte, M. B. Berkemeier, W. Sextro, and S. Peitz, “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings,” <i>Multibody System Dynamics</i>, pp. 1–21, 2026, doi: <a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>.","apa":"Wohlleben, M. C., Schütte, J., Berkemeier, M. B., Sextro, W., &#38; Peitz, S. (2026). Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings. <i>Multibody System Dynamics</i>, 1–21. <a href=\"https://doi.org/10.1007/s11044-026-10146-9\">https://doi.org/10.1007/s11044-026-10146-9</a>","ama":"Wohlleben MC, Schütte J, Berkemeier MB, Sextro W, Peitz S. Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings. <i>Multibody System Dynamics</i>. Published online 2026:1–21. doi:<a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>","short":"M.C. Wohlleben, J. Schütte, M.B. Berkemeier, W. Sextro, S. Peitz, Multibody System Dynamics (2026) 1–21.","bibtex":"@article{Wohlleben_Schütte_Berkemeier_Sextro_Peitz_2026, title={Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings}, DOI={<a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>}, journal={Multibody System Dynamics}, author={Wohlleben, Meike Claudia and Schütte, Jan and Berkemeier, Manuel Bastian and Sextro, Walter and Peitz, Sebastian}, year={2026}, pages={1–21} }","mla":"Wohlleben, Meike Claudia, et al. “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings.” <i>Multibody System Dynamics</i>, 2026, pp. 1–21, doi:<a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>."},"page":"1–21","year":"2026","quality_controlled":"1","publication_identifier":{"issn":["1384-5640"]},"doi":"10.1007/s11044-026-10146-9","title":"Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings","date_created":"2026-01-27T15:51:55Z","author":[{"orcid":"0009-0009-9767-7168","last_name":"Wohlleben","id":"43991","full_name":"Wohlleben, Meike Claudia","first_name":"Meike Claudia"},{"last_name":"Schütte","orcid":"0000-0001-9025-9742","id":"22109","full_name":"Schütte, Jan","first_name":"Jan"},{"first_name":"Manuel Bastian","last_name":"Berkemeier","full_name":"Berkemeier, Manuel Bastian"},{"first_name":"Walter","last_name":"Sextro","id":"21220","full_name":"Sextro, Walter"},{"first_name":"Sebastian","full_name":"Peitz, Sebastian","last_name":"Peitz"}],"date_updated":"2026-03-03T06:31:03Z"},{"_id":"57829","user_id":"43991","language":[{"iso":"eng"}],"type":"journal_article","publication":"Technische Mechanik - European Journal of Engineering Mechanics","abstract":[{"text":"Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program \\emph{SPP~2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics}, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics.","lang":"eng"}],"status":"public","date_updated":"2026-03-03T06:31:55Z","date_created":"2024-12-18T09:07:41Z","author":[{"full_name":"de Payrebrune, Kristin M.","last_name":"de Payrebrune","first_name":"Kristin M."},{"last_name":"Flaßkamp","full_name":"Flaßkamp, Kathrin","first_name":"Kathrin"},{"first_name":"Tom","last_name":"Ströhla","full_name":"Ströhla, Tom"},{"first_name":"Thomas","full_name":"Sattel, Thomas","last_name":"Sattel"},{"last_name":"Bestle","full_name":"Bestle, Dieter","first_name":"Dieter"},{"first_name":"Benedict","full_name":"Röder, Benedict","last_name":"Röder"},{"first_name":"Peter","last_name":"Eberhard","full_name":"Eberhard, Peter"},{"last_name":"Peitz","full_name":"Peitz, Sebastian","first_name":"Sebastian"},{"full_name":"Stoffel, Marcus","last_name":"Stoffel","first_name":"Marcus"},{"first_name":"Gulakala","last_name":"Rutwik","full_name":"Rutwik, Gulakala"},{"full_name":"Aditya, Borse","last_name":"Aditya","first_name":"Borse"},{"first_name":"Meike Claudia","orcid":"0009-0009-9767-7168","last_name":"Wohlleben","id":"43991","full_name":"Wohlleben, Meike Claudia"},{"first_name":"Walter","full_name":"Sextro, Walter","id":"21220","last_name":"Sextro"},{"last_name":"Raff","full_name":"Raff, Maximilian","first_name":"Maximilian"},{"first_name":"C. David","full_name":"Remy, C. David","last_name":"Remy"},{"full_name":"Yadav, Manish","last_name":"Yadav","first_name":"Manish"},{"first_name":"Merten","last_name":"Stender","full_name":"Stender, Merten"},{"last_name":"van Delden","full_name":"van Delden, Jan","first_name":"Jan"},{"first_name":"Timo","full_name":"Lüddecke, Timo","last_name":"Lüddecke"},{"full_name":"Langer, Sabine C.","last_name":"Langer","first_name":"Sabine C."},{"last_name":"Schultz","full_name":"Schultz, Julius","first_name":"Julius"},{"full_name":"Blech, Christopher","last_name":"Blech","first_name":"Christopher"}],"volume":45,"title":"The impact of AI on engineering design procedures for dynamical systems","doi":"10.24352/UB.OVGU-2025-037","quality_controlled":"1","issue":"1","year":"2025","citation":{"chicago":"Payrebrune, Kristin M. de, Kathrin Flaßkamp, Tom Ströhla, Thomas Sattel, Dieter Bestle, Benedict Röder, Peter Eberhard, et al. “The Impact of AI on Engineering Design Procedures for Dynamical Systems.” <i>Technische Mechanik - European Journal of Engineering Mechanics</i> 45, no. 1 (2025): 1–23. <a href=\"https://doi.org/10.24352/UB.OVGU-2025-037\">https://doi.org/10.24352/UB.OVGU-2025-037</a>.","ieee":"K. M. de Payrebrune <i>et al.</i>, “The impact of AI on engineering design procedures for dynamical systems,” <i>Technische Mechanik - European Journal of Engineering Mechanics</i>, vol. 45, no. 1, pp. 1–23, 2025, doi: <a href=\"https://doi.org/10.24352/UB.OVGU-2025-037\">10.24352/UB.OVGU-2025-037</a>.","ama":"de Payrebrune KM, Flaßkamp K, Ströhla T, et al. The impact of AI on engineering design procedures for dynamical systems. <i>Technische Mechanik - European Journal of Engineering Mechanics</i>. 2025;45(1):1-23. doi:<a href=\"https://doi.org/10.24352/UB.OVGU-2025-037\">10.24352/UB.OVGU-2025-037</a>","apa":"de Payrebrune, K. M., Flaßkamp, K., Ströhla, T., Sattel, T., Bestle, D., Röder, B., Eberhard, P., Peitz, S., Stoffel, M., Rutwik, G., Aditya, B., Wohlleben, M. C., Sextro, W., Raff, M., Remy, C. D., Yadav, M., Stender, M., van Delden, J., Lüddecke, T., … Blech, C. (2025). The impact of AI on engineering design procedures for dynamical systems. <i>Technische Mechanik - European Journal of Engineering Mechanics</i>, <i>45</i>(1), 1–23. <a href=\"https://doi.org/10.24352/UB.OVGU-2025-037\">https://doi.org/10.24352/UB.OVGU-2025-037</a>","bibtex":"@article{de Payrebrune_Flaßkamp_Ströhla_Sattel_Bestle_Röder_Eberhard_Peitz_Stoffel_Rutwik_et al._2025, title={The impact of AI on engineering design procedures for dynamical systems}, volume={45}, DOI={<a href=\"https://doi.org/10.24352/UB.OVGU-2025-037\">10.24352/UB.OVGU-2025-037</a>}, number={1}, journal={Technische Mechanik - European Journal of Engineering Mechanics}, author={de Payrebrune, Kristin M. and Flaßkamp, Kathrin and Ströhla, Tom and Sattel, Thomas and Bestle, Dieter and Röder, Benedict and Eberhard, Peter and Peitz, Sebastian and Stoffel, Marcus and Rutwik, Gulakala and et al.}, year={2025}, pages={1–23} }","short":"K.M. de Payrebrune, K. Flaßkamp, T. Ströhla, T. Sattel, D. Bestle, B. Röder, P. Eberhard, S. Peitz, M. Stoffel, G. Rutwik, B. Aditya, M.C. Wohlleben, W. Sextro, M. Raff, C.D. Remy, M. Yadav, M. Stender, J. van Delden, T. Lüddecke, S.C. Langer, J. Schultz, C. Blech, Technische Mechanik - European Journal of Engineering Mechanics 45 (2025) 1–23.","mla":"de Payrebrune, Kristin M., et al. “The Impact of AI on Engineering Design Procedures for Dynamical Systems.” <i>Technische Mechanik - European Journal of Engineering Mechanics</i>, vol. 45, no. 1, 2025, pp. 1–23, doi:<a href=\"https://doi.org/10.24352/UB.OVGU-2025-037\">10.24352/UB.OVGU-2025-037</a>."},"intvolume":"        45","page":"1-23"},{"place":"Cham","year":"2025","citation":{"bibtex":"@inbook{Amakor_Berkemeier_Wohlleben_Sextro_Peitz_2025, place={Cham}, title={Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">10.1007/978-3-032-04555-3_21</a>}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland}, author={Amakor, Augustina C. and Berkemeier, Manuel B. and Wohlleben, Meike Claudia and Sextro, Walter and Peitz, Sebastian}, year={2025} }","short":"A.C. Amakor, M.B. Berkemeier, M.C. Wohlleben, W. Sextro, S. Peitz, in: Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.","mla":"Amakor, Augustina C., et al. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” <i>Lecture Notes in Computer Science</i>, Springer Nature Switzerland, 2025, doi:<a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">10.1007/978-3-032-04555-3_21</a>.","apa":"Amakor, A. C., Berkemeier, M. B., Wohlleben, M. C., Sextro, W., &#38; Peitz, S. (2025). Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. In <i>Lecture Notes in Computer Science</i>. Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">https://doi.org/10.1007/978-3-032-04555-3_21</a>","ama":"Amakor AC, Berkemeier MB, Wohlleben MC, Sextro W, Peitz S. Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. In: <i>Lecture Notes in Computer Science</i>. Springer Nature Switzerland; 2025. doi:<a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">10.1007/978-3-032-04555-3_21</a>","ieee":"A. C. Amakor, M. B. Berkemeier, M. C. Wohlleben, W. Sextro, and S. Peitz, “Surrogate-Assisted Multi-objective Design of Complex Multibody Systems,” in <i>Lecture Notes in Computer Science</i>, Cham: Springer Nature Switzerland, 2025.","chicago":"Amakor, Augustina C., Manuel B. Berkemeier, Meike Claudia Wohlleben, Walter Sextro, and Sebastian Peitz. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” In <i>Lecture Notes in Computer Science</i>. Cham: Springer Nature Switzerland, 2025. <a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">https://doi.org/10.1007/978-3-032-04555-3_21</a>."},"publication_identifier":{"isbn":["9783032045546","9783032045553"],"issn":["0302-9743","1611-3349"]},"quality_controlled":"1","publication_status":"published","title":"Surrogate-Assisted Multi-objective Design of Complex Multibody Systems","doi":"10.1007/978-3-032-04555-3_21","publisher":"Springer Nature Switzerland","date_updated":"2026-03-03T06:32:10Z","author":[{"first_name":"Augustina C.","last_name":"Amakor","full_name":"Amakor, Augustina C."},{"full_name":"Berkemeier, Manuel B.","last_name":"Berkemeier","first_name":"Manuel B."},{"first_name":"Meike Claudia","id":"43991","full_name":"Wohlleben, Meike Claudia","orcid":"0009-0009-9767-7168","last_name":"Wohlleben"},{"last_name":"Sextro","full_name":"Sextro, Walter","id":"21220","first_name":"Walter"},{"first_name":"Sebastian","full_name":"Peitz, Sebastian","last_name":"Peitz"}],"date_created":"2025-12-09T12:46:17Z","status":"public","publication":"Lecture Notes in Computer Science","type":"book_chapter","language":[{"iso":"eng"}],"_id":"62988","department":[{"_id":"151"}],"user_id":"43991"},{"type":"preprint","publication_status":"published","status":"public","citation":{"short":"M.C. Wohlleben, J.M. Linneweber, J. Schütte, W. Sextro, (2025).","bibtex":"@article{Wohlleben_Linneweber_Schütte_Sextro_2025, title={Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach}, publisher={MDPI AG}, author={Wohlleben, Meike Claudia and Linneweber, Jill Mercedes and Schütte, Jan and Sextro, Walter}, year={2025} }","mla":"Wohlleben, Meike Claudia, et al. <i>Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach</i>. MDPI AG, 2025.","apa":"Wohlleben, M. C., Linneweber, J. M., Schütte, J., &#38; Sextro, W. (2025). <i>Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach</i>. MDPI AG.","ama":"Wohlleben MC, Linneweber JM, Schütte J, Sextro W. Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach. Published online 2025.","ieee":"M. C. Wohlleben, J. M. Linneweber, J. Schütte, and W. Sextro, “Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach.” MDPI AG, 2025.","chicago":"Wohlleben, Meike Claudia, Jill Mercedes Linneweber, Jan Schütte, and Walter Sextro. “Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach.” MDPI AG, 2025."},"abstract":[{"text":"<jats:p>Hybrid modeling aims to combine physical and data-driven models to increase simulation accuracy without losing physical interpretability. In the context of dynamic mechanical systems, this enables the compensation of modeling inaccuracies that arise from simplifications, missing effects, or uncertain parameters. In this work, a hybrid model is used as a starting point, in which the discrepancy between simulation and measurement is learned and compensated by a data-driven correction element. To integrate such models into commercial multibody system (MBS) software like MSC Adams and Simpack, the formulation is adapted to operate directly on the force level. This allows implementation via standard co-simulation interfaces without modifying the system’s differential equations or solvers. The method is demonstrated using a single-mass oscillator with synthetic measurement data. Results show that the coupled simulation works reliably and that the hybrid model significantly improves accuracy while remaining compatible with established industrial simulation workflows.</jats:p>","lang":"eng"}],"year":"2025","user_id":"43991","author":[{"full_name":"Wohlleben, Meike Claudia","id":"43991","last_name":"Wohlleben","orcid":"0009-0009-9767-7168","first_name":"Meike Claudia"},{"last_name":"Linneweber","full_name":"Linneweber, Jill Mercedes","id":"57639","first_name":"Jill Mercedes"},{"first_name":"Jan","last_name":"Schütte","orcid":"0000-0001-9025-9742","full_name":"Schütte, Jan","id":"22109"},{"id":"21220","full_name":"Sextro, Walter","last_name":"Sextro","first_name":"Walter"}],"date_created":"2025-08-06T06:42:30Z","_id":"60881","publisher":"MDPI AG","date_updated":"2025-08-07T06:47:23Z","language":[{"iso":"eng"}],"title":"Enabling Hybrid Modeling in Commercial MBS Software: A Force-Level Approach"},{"quality_controlled":"1","page":"e202400027","citation":{"mla":"Wohlleben, Meike Claudia, et al. “Hybrid Modeling of Multibody Systems: Comparison of Two Discrepancy Models for Trajectory Prediction.” <i>PAMM</i>, 2024, p. e202400027, doi:<a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>.","short":"M.C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, P. Eberhard, PAMM (2024) e202400027.","bibtex":"@article{Wohlleben_Röder_Ebel_Muth_Sextro_Eberhard_2024, title={Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction}, DOI={<a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>}, journal={PAMM}, author={Wohlleben, Meike Claudia and Röder, Benedict and Ebel, Henrik and Muth, Lars and Sextro, Walter and Eberhard, Peter}, year={2024}, pages={e202400027} }","apa":"Wohlleben, M. C., Röder, B., Ebel, H., Muth, L., Sextro, W., &#38; Eberhard, P. (2024). Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction. <i>PAMM</i>, e202400027. <a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>","ama":"Wohlleben MC, Röder B, Ebel H, Muth L, Sextro W, Eberhard P. Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction. <i>PAMM</i>. Published online 2024:e202400027. doi:<a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>","ieee":"M. C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, and P. Eberhard, “Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction,” <i>PAMM</i>, p. e202400027, 2024, doi: <a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>.","chicago":"Wohlleben, Meike Claudia, Benedict Röder, Henrik Ebel, Lars Muth, Walter Sextro, and Peter Eberhard. “Hybrid Modeling of Multibody Systems: Comparison of Two Discrepancy Models for Trajectory Prediction.” <i>PAMM</i>, 2024, e202400027. <a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>."},"year":"2024","date_created":"2024-09-11T13:38:03Z","author":[{"full_name":"Wohlleben, Meike Claudia","id":"43991","orcid":"0009-0009-9767-7168","last_name":"Wohlleben","first_name":"Meike Claudia"},{"last_name":"Röder","full_name":"Röder, Benedict","first_name":"Benedict"},{"first_name":"Henrik","full_name":"Ebel, Henrik","last_name":"Ebel"},{"first_name":"Lars","last_name":"Muth","orcid":"0000-0002-2938-5616","full_name":"Muth, Lars","id":"77313"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"},{"first_name":"Peter","last_name":"Eberhard","full_name":"Eberhard, Peter"}],"date_updated":"2025-02-27T19:54:08Z","doi":"https://doi.org/10.1002/pamm.202400027","title":"Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction","publication":"PAMM","type":"journal_article","status":"public","abstract":[{"text":"Abstract This study focuses on hybrid modeling approaches that combine physical and data-driven methods to create more effective dynamical system models. In particular, it examines discrepancy models, a type of hybrid model that integrates a physical system model with data-driven compensation for inaccuracies. The study applies two discrepancy modeling methods to a multibody system using discrepancies in the state vector and its time derivative, respectively. As an application example, a four-bar linkage with nonlinear damping is investigated, using a simplified conservative system as a physical model. The comparative analysis of the two methods shows that the continuous approach generally outperforms the discrete method in terms of accuracy and computational efficiency, especially for velocity prediction and prediction horizon. However, scenarios, where input signals for training and testing differ, present nuanced findings. When the continuous method is trained on complex signals (sine) and tested on simpler ones (stair), it struggles to deliver satisfactory results, exhibiting notably higher root mean square error (RMSE) values, particularly in angular velocity prediction. Conversely, training on simple signals (stair) and testing on complex ones (sine) surprisingly yields low RMSE values, indicating the continuous method’s adaptability. While the discrete method aligns more closely with expectations and performs better in certain scenarios, its results are consistently moderate, neither exceptional nor particularly poor. The study also introduces a selection framework for choosing the most suitable algorithm based on the specific characteristics of the modeling task. This framework provides guidance for researchers and practitioners in leveraging hybrid modeling effectively. Finally, the study concludes with an outlook on future research directions.","lang":"eng"}],"user_id":"77313","_id":"56113","language":[{"iso":"eng"}]},{"user_id":"77313","department":[{"_id":"655"},{"_id":"151"}],"_id":"46813","language":[{"iso":"eng"}],"keyword":["Electrical and Electronic Engineering","Atomic and Molecular Physics","and Optics"],"type":"conference","publication":"Proceedings in Applied Mathematics and Mechanics","status":"public","abstract":[{"lang":"eng","text":"Modelling of dynamic systems plays an important role in many engineering disciplines. Two different approaches are physical modelling and data‐driven modelling, both of which have their respective advantages and disadvantages. By combining these two approaches, hybrid models can be created in which the respective disadvantages are mitigated, with discrepancy models being a particular subclass. Here, the basic system behaviour is described physically, that is, in the form of differential equations. Inaccuracies resulting from insufficient modelling or numerics lead to a discrepancy between the measurements and the model, which can be compensated by a data‐driven error correction term. Since discrepancy methods still require a large amount of measurement data, this paper investigates the extent to which a single discrepancy model can be trained for a physical model with additional parameter dependencies without the need for retraining. As an example, a damped electromagnetic oscillating circuit is used. The physical model is realised by a differential equation describing the electric current, considering only inductance and capacitance; dissipation due to resistance is neglected. This creates a discrepancy between measurement and model, which is corrected by a data‐driven model. In the experiments, the inductance and the capacity are varied. It is found that the same data‐driven model can only be used if additional parametric dependencies in the data‐driven term are considered as well."}],"date_created":"2023-09-06T05:18:05Z","author":[{"first_name":"Meike Claudia","last_name":"Wohlleben","orcid":"0009-0009-9767-7168","id":"43991","full_name":"Wohlleben, Meike Claudia"},{"first_name":"Lars","orcid":"0000-0002-2938-5616","last_name":"Muth","id":"77313","full_name":"Muth, Lars"},{"first_name":"Sebastian","last_name":"Peitz","orcid":"0000-0002-3389-793X","id":"47427","full_name":"Peitz, Sebastian"},{"first_name":"Walter","full_name":"Sextro, Walter","id":"21220","last_name":"Sextro"}],"oa":"1","publisher":"Wiley","date_updated":"2023-09-21T14:47:20Z","main_file_link":[{"open_access":"1","url":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202300039"}],"doi":"10.1002/pamm.202300039","title":"Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits","publication_status":"published","quality_controlled":"1","publication_identifier":{"issn":["1617-7061","1617-7061"]},"citation":{"apa":"Wohlleben, M. C., Muth, L., Peitz, S., &#38; Sextro, W. (2023). Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. <i>Proceedings in Applied Mathematics and Mechanics</i>. <a href=\"https://doi.org/10.1002/pamm.202300039\">https://doi.org/10.1002/pamm.202300039</a>","bibtex":"@inproceedings{Wohlleben_Muth_Peitz_Sextro_2023, title={Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits}, DOI={<a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>}, booktitle={Proceedings in Applied Mathematics and Mechanics}, publisher={Wiley}, author={Wohlleben, Meike Claudia and Muth, Lars and Peitz, Sebastian and Sextro, Walter}, year={2023} }","short":"M.C. Wohlleben, L. Muth, S. Peitz, W. Sextro, in: Proceedings in Applied Mathematics and Mechanics, Wiley, 2023.","mla":"Wohlleben, Meike Claudia, et al. “Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating Circuits.” <i>Proceedings in Applied Mathematics and Mechanics</i>, Wiley, 2023, doi:<a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>.","ama":"Wohlleben MC, Muth L, Peitz S, Sextro W. Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. In: <i>Proceedings in Applied Mathematics and Mechanics</i>. Wiley; 2023. doi:<a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>","chicago":"Wohlleben, Meike Claudia, Lars Muth, Sebastian Peitz, and Walter Sextro. “Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating Circuits.” In <i>Proceedings in Applied Mathematics and Mechanics</i>. Wiley, 2023. <a href=\"https://doi.org/10.1002/pamm.202300039\">https://doi.org/10.1002/pamm.202300039</a>.","ieee":"M. C. Wohlleben, L. Muth, S. Peitz, and W. Sextro, “Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits,” 2023, doi: <a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>."},"year":"2023"},{"place":"Cham","year":"2022","citation":{"ama":"Wohlleben MC, Bender A, Peitz S, Sextro W. Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. In: <i>Machine Learning, Optimization, and Data Science</i>. Springer International Publishing; 2022. doi:<a href=\"https://doi.org/10.1007/978-3-030-95470-3_8\">10.1007/978-3-030-95470-3_8</a>","chicago":"Wohlleben, Meike Claudia, Amelie Bender, Sebastian Peitz, and Walter Sextro. “Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction.” In <i>Machine Learning, Optimization, and Data Science</i>. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-030-95470-3_8\">https://doi.org/10.1007/978-3-030-95470-3_8</a>.","ieee":"M. C. Wohlleben, A. Bender, S. Peitz, and W. Sextro, “Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction,” in <i>Machine Learning, Optimization, and Data Science</i>, Cham: Springer International Publishing, 2022.","bibtex":"@inbook{Wohlleben_Bender_Peitz_Sextro_2022, place={Cham}, title={Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-95470-3_8\">10.1007/978-3-030-95470-3_8</a>}, booktitle={Machine Learning, Optimization, and Data Science}, publisher={Springer International Publishing}, author={Wohlleben, Meike Claudia and Bender, Amelie and Peitz, Sebastian and Sextro, Walter}, year={2022} }","mla":"Wohlleben, Meike Claudia, et al. “Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction.” <i>Machine Learning, Optimization, and Data Science</i>, Springer International Publishing, 2022, doi:<a href=\"https://doi.org/10.1007/978-3-030-95470-3_8\">10.1007/978-3-030-95470-3_8</a>.","short":"M.C. Wohlleben, A. Bender, S. Peitz, W. Sextro, in: Machine Learning, Optimization, and Data Science, Springer International Publishing, Cham, 2022.","apa":"Wohlleben, M. C., Bender, A., Peitz, S., &#38; Sextro, W. (2022). Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction. In <i>Machine Learning, Optimization, and Data Science</i>. Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-95470-3_8\">https://doi.org/10.1007/978-3-030-95470-3_8</a>"},"publication_identifier":{"isbn":["9783030954697","9783030954703"],"issn":["0302-9743","1611-3349"]},"quality_controlled":"1","publication_status":"published","title":"Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction","doi":"10.1007/978-3-030-95470-3_8","main_file_link":[{"url":"https://link.springer.com/content/pdf/10.1007%2F978-3-030-95470-3_8.pdf"}],"publisher":"Springer International Publishing","date_updated":"2023-04-26T12:10:58Z","date_created":"2022-02-03T10:30:23Z","author":[{"first_name":"Meike Claudia","last_name":"Wohlleben","full_name":"Wohlleben, Meike Claudia","id":"43991"},{"first_name":"Amelie","last_name":"Bender","full_name":"Bender, Amelie","id":"54290"},{"first_name":"Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","full_name":"Peitz, Sebastian","id":"47427"},{"first_name":"Walter","full_name":"Sextro, Walter","id":"21220","last_name":"Sextro"}],"status":"public","publication":"Machine Learning, Optimization, and Data Science","type":"book_chapter","language":[{"iso":"eng"}],"_id":"29727","department":[{"_id":"151"},{"_id":"655"}],"user_id":"43991"},{"quality_controlled":"1","issue":"1","year":"2021","date_created":"2021-11-03T12:26:39Z","title":"Rule-based Diagnostics of a Production Line","publication":"Proceedings of the European Conference of the PHM Society 2021","abstract":[{"text":"In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions.","lang":"eng"}],"keyword":["PHME 2021","Feature Selection Classification","Feature Selection Clustering","Interpretable Model","Transparent Model","Industry 4.0","Real-World Diagnostics","Quality Control","Predictive Maintenance"],"language":[{"iso":"eng"}],"publication_status":"published","page":"527-536","intvolume":"         6","citation":{"chicago":"Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In <i>Proceedings of the European Conference of the PHM Society 2021</i>, edited by Phuc Do, Steve King, and Olga Fink, 6:527–36, 2021. <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">https://doi.org/10.36001/phme.2021.v6i1.3042</a>.","ieee":"O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based Diagnostics of a Production Line,” in <i>Proceedings of the European Conference of the PHM Society 2021</i>, 2021, vol. 6, no. 1, pp. 527–536, doi: <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>.","ama":"Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics of a Production Line. In: Do P, King S, Fink O, eds. <i>Proceedings of the European Conference of the PHM Society 2021</i>. Vol 6. ; 2021:527-536. doi:<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>","apa":"Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., &#38; Sextro, W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &#38; O. Fink (Eds.), <i>Proceedings of the European Conference of the PHM Society 2021</i> (Vol. 6, Issue 1, pp. 527–536). <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">https://doi.org/10.36001/phme.2021.v6i1.3042</a>","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production Line.” <i>Proceedings of the European Conference of the PHM Society 2021</i>, edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>.","bibtex":"@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based Diagnostics of a Production Line}, volume={6}, DOI={<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>}, number={1}, booktitle={Proceedings of the European Conference of the PHM Society 2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve and Fink, Olga}, year={2021}, pages={527–536} }","short":"O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P. Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021, 2021, pp. 527–536."},"oa":"1","date_updated":"2023-09-22T09:13:01Z","volume":6,"author":[{"first_name":"Osarenren Kennedy","full_name":"Aimiyekagbon, Osarenren Kennedy","id":"9557","last_name":"Aimiyekagbon"},{"full_name":"Muth, Lars","id":"77313","last_name":"Muth","orcid":"0000-0002-2938-5616","first_name":"Lars"},{"first_name":"Meike Claudia","id":"43991","full_name":"Wohlleben, Meike Claudia","last_name":"Wohlleben","orcid":"0009-0009-9767-7168"},{"first_name":"Amelie","id":"54290","full_name":"Bender, Amelie","last_name":"Bender"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"}],"doi":"10.36001/phme.2021.v6i1.3042","conference":{"name":"PHM Society European Conference"},"main_file_link":[{"url":"http://papers.phmsociety.org/index.php/phme/article/download/3042/1812","open_access":"1"}],"type":"conference","editor":[{"last_name":"Do","full_name":"Do, Phuc","first_name":"Phuc"},{"last_name":"King","full_name":"King, Steve","first_name":"Steve"},{"first_name":"Olga","last_name":"Fink","full_name":"Fink, Olga"}],"status":"public","_id":"27111","department":[{"_id":"151"}],"user_id":"9557"}]