A5074970467- Structural Health Monitoring Techniques
- Infrastructure Maintenance and Monitoring
- Model Reduction and Neural Networks
- Adhesion, Friction, and Surface Interactions
- Advanced machining processes and optimization
- Magnetic Properties and Applications
- Surface Roughness and Optical Measurements
- Non-Destructive Testing Techniques
- Advanced Fiber Optic Sensors
- Brake Systems and Friction Analysis
- Vehicle emissions and performance
- Tunneling and Rock Mechanics
- Natural Language Processing Techniques
- Integrated Circuits and Semiconductor Failure Analysis
- Linguistics and Cultural Studies
- BIM and Construction Integration
- Manufacturing Process and Optimization
- Educational Methods and Analysis
Universität der Bundeswehr München
2023-2024
Abstract Digital twins map physical objects, processes, and further entities from the real (physical) world into digital space. Going one step further, hybrid combine physics‐based modeling with data‐based techniques to form a simulation tool predictive power. In light of an increasing digitalization our built world, such have great potential contribute protection critical technical infrastructures. case bridges, can key role in structural health monitoring. This contribution outlines path...
In this study, we investigate the potential of fast-to-evaluate surrogate modeling techniques for developing a hybrid digital twin steel-reinforced concrete beam, serving as representative example civil engineering structure. As surrogates, two distinct models are developed utilizing physics-informed neural networks, which integrate experimental data with given governing laws physics. The (sensor data) is obtained from previously conducted four-point bending test. first model predicts...
In this study, we investigate the potential of fast-to-evaluate surrogate modeling techniques that fuse sensor data with non-sensor information, i.e. underlying physics, for developing a hybrid digital twin steel-reinforced concrete beam, serving as representative example civil engineering structure such bridge. Bridges are critical infrastructures require continuous monitoring and maintenance predictive power to ensure their safety longevity. Therefore, there is high demand models combine...
This paper explores the application of physics-informed neural networks (PINNs) to tackle forward problems in 3D contact mechanics, focusing on small deformation elasticity. We utilize a mixed-variable formulation, enhanced with output transformations, enforce Dirichlet and Neumann boundary conditions as hard constraints. The inherent inequality constraints particularly Karush-Kuhn-Tucker (KKT) conditions, are addressed soft by integrating them into network's loss function. To KKT we...
ÖzBu çalışma cümleden anlam çıkarılması ve Türkçe metinlerin çizge veri yapısında temsil edilmesi ile ilgili yaklaşımları
Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems resilient ecosystems, civil infrastructures, smart cities, healthcare. They have become indispensable for solving complex problems in science, engineering, technology development. The purpose MMLDT-CSET 2021 conference is facilitate transition ML DT fundamental research mainstream fields technologies through data mechanistic methods,...