A5062277879- Robotic Mechanisms and Dynamics
- Manufacturing Process and Optimization
- Advanced Measurement and Metrology Techniques
- Gear and Bearing Dynamics Analysis
- Model Reduction and Neural Networks
- Robotic Locomotion and Control
- Mechanical Engineering and Vibrations Research
- Chaos, Complexity, and Education
- Advanced Clustering Algorithms Research
- Image Retrieval and Classification Techniques
- Face and Expression Recognition
- Hydraulic and Pneumatic Systems
- Probabilistic and Robust Engineering Design
- Mechanics and Biomechanics Studies
University of Stuttgart
2023-2025
University of Tübingen
2019
<title>Abstract</title> We investigate the synthesis of crank-driven four-bar linkages by learning inverse problem predicting mechanism parameters from given coupler-point paths. This exhibits an ambiguity in feature space, where different mechanisms may produce very similar paper proposes a dual-network approach to combat this issue and compares it naive single-network approach. Furthermore, extraction methods, normalizations, lengths are evaluated. show that generally leads better more...
Abstract Data-based methods have gained increasing importance in engineering. Success stories are prevalent areas such as data-driven modeling, control, and automation, well surrogate modeling for accelerated simulation. Beyond engineering, generative large-language models increasingly helping with tasks that, previously, were solely associated creative human processes. Thus, it seems timely to seek artificial-intelligence-support engineering design automate, help with, or accelerate...
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...
Abstract More and more affordable high-throughput techniques for measuring molecular features of biomedical samples have led to a huge increase in availability size different types multi-omic datasets, containing, example, genetic or histone modification data. Due the multi-view characteristic data, established approaches exploratory analysis are not directly applicable. Here we present web-rMKL, web server that provides an integrative dimensionality reduction with subsequent clustering...
Data-based methods have gained increasing importance in engineering, especially but not only driven by successes with deep artificial neural networks. Success stories are prevalent, e.g., areas such as data-driven modeling, control and automation, well surrogate modeling for accelerated simulation. Beyond generative large-language models increasingly performing helping tasks that, previously, were solely associated creative human processes. Thus, it seems timely to seek...
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...
Abstract General‐purpose mechanisms can perform a broad range of tasks but are usually rather heavy and expensive. If only particular movements need to be executed, more efficient special‐purpose employed. However, they typically require an expert design the system based on manual inspection simulations experimental results. This procedure is not time‐consuming, outcome also depends expert's experience. Hence, process stems from subjective criteria while limited number structurally different...