{"author":[{"id":"43991","last_name":"Wohlleben","first_name":"Meike Claudia","orcid":"0009-0009-9767-7168","full_name":"Wohlleben, Meike Claudia"},{"orcid":"0000-0001-9025-9742","full_name":"Schütte, Jan","last_name":"Schütte","first_name":"Jan","id":"22109"},{"last_name":"Berkemeier","first_name":"Manuel Bastian","full_name":"Berkemeier, Manuel Bastian"},{"id":"21220","full_name":"Sextro, Walter","last_name":"Sextro","first_name":"Walter"},{"full_name":"Peitz, Sebastian","first_name":"Sebastian","last_name":"Peitz"}],"citation":{"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} }","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>.","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>","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>.","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>"},"title":"Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings","type":"journal_article","date_updated":"2026-01-27T15:53:36Z","language":[{"iso":"eng"}],"user_id":"43991","publication_identifier":{"issn":["1384-5640"]},"department":[{"_id":"151"}],"publication":"Multibody System Dynamics","year":"2026","page":"1–21","_id":"63765","doi":"10.1007/s11044-026-10146-9","status":"public","date_created":"2026-01-27T15:51:55Z","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."}]}