A5071639719- Model Reduction and Neural Networks
- Mechanical Engineering and Vibrations Research
- Probabilistic and Robust Engineering Design
- Hydraulic and Pneumatic Systems
- Electromagnetic Simulation and Numerical Methods
- Mechanical Failure Analysis and Simulation
- Vehicle Dynamics and Control Systems
- Mechanical stress and fatigue analysis
- Dynamics and Control of Mechanical Systems
- Manufacturing Process and Optimization
- Magnetic Properties and Applications
Paderborn University
2022-2025
<title>Abstract</title> Rubber-metal bushings (RMB) are critical components in multi-body systems, such as vehicles and industrial machinery, due to their abilityto enable relative motion, dampen vibrations, transmit forces. However,their nonlinear behavior challenges accurate modeling. Traditional physics-based models often fail balance simplicity, accuracy, computationalefficiency. The growing availability of experimental data offers opportunitiesto improve RMB modeling through hybrid...
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 model integrates with compensation for inaccuracies. The applies two multibody using discrepancies in the state vector its time derivative, respectively. As an application example, four‐bar linkage nonlinear damping is investigated, simplified conservative as model. comparative...
The optimization of large-scale multibody systems is a numerically challenging task, in particular when considering multiple conflicting criteria at the same time. In this situation, we need to approximate Pareto set optimal compromises, which significantly more expensive than finding single optimum single-objective optimization. To prevent large costs, usage surrogate models, constructed from small but informative number model evaluations, very popular and widely studied approach. central...
Abstract Modelling of dynamic systems plays an important role in many engineering disciplines. Two different approaches are physical modelling and data‐driven modelling, both which have their respective advantages disadvantages. By combining these two approaches, hybrid models can be created the disadvantages mitigated, with discrepancy being a particular subclass. Here, basic system behaviour is described physically, that is, form differential equations. Inaccuracies resulting from...
Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems undergoing a similar transformation. Over the past decade, modeling, simulation, optimization techniques have become integral to design process, paving way adoption AI-based methods. In this paper, we examine potential integrating AI into engineering using V-model from VDI guideline...