TY - GEN A1 - Rui, Xue A1 - Bestle, Dieter T1 - Reduced Multibody System Transfer Matrix Method Using Decoupled Hinge Equations T2 - International journal of mechanical system dynamics : IJMSD Y1 - 2021 U6 - https://doi.org/10.1002/msd2.12026 SN - 2767-1402 VL - 1 IS - 2 SP - 182 EP - 193 ER - TY - GEN A1 - Zhou, Qinbo A1 - Fehr, Jörg A1 - Bestle, Dieter A1 - Rui, Xiaoting T1 - Simulation of Elastic Body Dynamics with Large Motion Using Transfer Matrix Method T2 - Multibody System Dynamics Y1 - 2022 U6 - https://doi.org/10.1007/s11044-022-09869-2 SN - 1573-272X SN - 1384-5640 VL - 59 (2023) IS - 3 SP - 269 EP - 292 ER - TY - GEN A1 - de Payrebrune, Kristin A1 - Flaßkamp, Kathrin A1 - Ströhla, Tom A1 - Sattel, Thomas A1 - Bestle, Dieter A1 - al, et T1 - The impact of Al on engineering design procedures for dynamical systems T2 - Electrical Engineering and Systems Science. Systems and Control Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2412.12230 PB - Cornell University ER - TY - GEN A1 - Zhai, Tianyu A1 - Bestle, Dieter T1 - Design of two coupled fuzzy controllers for a planar direct internat reforming solid oxide fuel cell T2 - Proceedings in Applied Mathematics and Mechanics Y1 - 2024 U6 - https://doi.org/10.1002/pamm.202400077 VL - 24 IS - 4 PB - Wiley-VCH GmbH ER - TY - CHAP A1 - Bestle, Dieter ED - Nachbagauer, Karin ED - Held, Alexander T1 - Optimization processes for automated design of industrial systems T2 - Optimal design and control of multibody systems : proceedings of the IUTAM symposium Y1 - 2024 SN - 978-3-031-49999-9 U6 - https://doi.org/10.1007/978-3-031-50000-8_1 SN - 1875-3507 SP - 3 EP - 15 PB - Springer CY - Hamburg ER - TY - GEN A1 - Kayatas, Zafer A1 - Bestle, Dieter T1 - Scenario identification and classification to support the assessment of advanced driver assistance systems T2 - Applied Mechanics N2 - In recent years, driver assistance systems in cars, buses, and trucks have become more common and powerful. In particular, the introduction of AI methods to sensors, signal fusion, and traffic recognition allows us to step forward from actual level-2 assistance to level-3 A dvanced D river A ssistance S ystems (ADAS), where driving becomes autonomous and responsibility shifts from the driver to the automobile manufacturers. This, however, requires a high-precision risk assessment of failure, which can only be achieved by extensive data acquisition and statistical analysis of real traffic scenarios (which is impossible to perform by humans). Therefore, critical driving situations have to be identified and classified automatically. This paper develops and compares two different strategies—a traditional rule-based approach derived from deterministic causal considerations, and an AI-based approach trained with idealized cut-in, cut-out, and cut-through maneuvers. Application to a 10-h measurement sequence on a German highway demonstrates that the latter has the higher performance, whereas the former misses some of the safety-relevant events to be identified. KW - advanced driver assistance system KW - real traffic situation KW - decision tree KW - machine learning KW - scenario identification KW - scenario classification Y1 - 2024 U6 - https://doi.org/10.3390/applmech5030032 SN - 2673-3161 VL - 5 IS - 3 SP - 563 EP - 578 PB - MDPI ER - TY - GEN A1 - Bielitz, Timo A1 - Bestle, Dieter T1 - Artificial recurrent model for parameter identification of dynamic systems T2 - Proceedings in Applied Mathematics & Mechanics Y1 - 2024 U6 - https://doi.org/10.1002/pamm.202400015 SN - 1617-7061 PB - Wiley-VCH GmbH ER - TY - GEN A1 - Bestle, Dieter A1 - Bielitz, Timo T1 - Real‐time models for systems with costly or unknown dynamics T2 - Special Issue: 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) N2 - Many applications require real‐time simulation where the computer model can be executed at least as fast as the underlying physical system. However, as the complexity of models grows, for example, in case of flexible multibody dynamics accounting for body elasticity, this becomes harder even with high‐end computers and parallel computing. Further the modeling itself could be too cumbersome to come up with causal models, especially in industrial applications, or modeling is inhibited by unknown physical effects. In such cases, data‐based modeling with AI‐strategies may be an alternative to come up with real‐time capable simulation models. Based on measured or costly simulated trajectories, state function values of the dynamic system are first identified from divided differences of subsequent states, and then used for training a feedforward neural network. This can then be used as an approximate surrogate for further simulations or as part of a real‐time application. Applications to the Duffing equation and a closed‐loop mechanism demonstrate high conformity of predicted and directly simulated trajectories reproducing even the bifurcation behavior of the Duffing equation. Y1 - 2024 U6 - https://doi.org/10.1002/pamm.202400008 SN - 1617-7061 VL - 24 IS - 2 ER - TY - GEN A1 - Bielitz, Timo A1 - Bestle, Dieter T1 - Identification of dynamic systems assisted by an autoregressive recurrent model T2 - Proceedings in Applied Mathematics & Mechanics : PAMM N2 - The identification of parameters in dynamic systems usually first requires modeling the system as nonlinear differential equations based on physical principles, which then can be evaluated to search for a set of optimal parameters that enable the mathematical model to reproduce some desired behavior of the real system. To overcome the necessity of complex modeling and to possibly reduce the number of extensive experimental evaluations of the real system or numerical evaluations of the differential equations, strategies from machine learning may be applied. The proposed method uses recurrent architectures such as Long Short-Term Memory (LSTM) networks as core to predict the system development from initial conditions and parameter values. The trainable weights of the model are optimized based on a set of training data containing parameter values and corresponding solution trajectories generated by evaluation of the system to be investigated. The trained model may then be used to identify unknown system parameter values related to a specific solution trajectory by solving an optimization problem for the inputs of the machine learning model. Y1 - 2023 U6 - https://doi.org/10.1002/pamm.202300086 SN - 1617-7061 VL - 23 IS - 2 ER - TY - GEN A1 - Niehoff, Malte A1 - Bestle, Dieter A1 - Kupijai, Philipp T1 - Model-based design optimization taking into account design viability via classification T2 - Engineering Modelling, Analysis and Simulation : the NAFEMS Journal Y1 - 2024 U6 - https://doi.org/10.59972/c7b5hzx7 SN - 3005-9763 VL - 1 IS - 1 SP - 1 EP - 12 PB - NAFEMS Ltd. CY - Knutsford ER -