A5071534698- Model Reduction and Neural Networks
- Neural Networks and Applications
- Model-Driven Software Engineering Techniques
- Reinforcement Learning in Robotics
- Machine Learning in Materials Science
- 3D Shape Modeling and Analysis
- Manufacturing Process and Optimization
- Generative Adversarial Networks and Image Synthesis
- Computer Graphics and Visualization Techniques
- Simulation Techniques and Applications
- Real-time simulation and control systems
- Advanced machining processes and optimization
Karlsruhe Institute of Technology
2023
Engineering components require an optimization of design and manufacturing parameters to achieve maximum performance – usually involving numerous physics-based simulations. Optimizing these is a resource-intensive endeavor, though, especially in high-dimensional scenarios or for complex materials like fiber reinforced plastics. Surrogate models are able reduce the computational effort, however, data generation still proves be resource-intensive. Additionally, their data-driven nature may...
Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy. Classical methods AMR depend on heuristics or expensive error estimators, hindering their use simulations. Recent learning-based tackle these issues, but so far scale only to simple toy examples. We formulate as novel Swarm Markov...
Engineering components must meet increasing technological demands in ever shorter development cycles. To face these challenges, a holistic approach is essential that allows for the concurrent of part design, material system and manufacturing process. Current approaches employ numerical simulations, which however quickly becomes computation-intensive, especially iterative optimization. Data-driven machine learning methods can be used to replace time- resource-intensive simulations. In...
Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element Method (FEM) widely used. However, FEM becomes computationally expensive as problem complexity and accuracy demands increase. Adaptive Mesh Refinement (AMR) improves by dynamically allocating mesh elements on domain, balancing computational speed accuracy. Classical AMR depends heuristics or error estimators,...
Many engineering systems require accurate simulations of complex physical systems. Yet, analytical solutions are only available for simple problems, necessitating numerical approximations such as the Finite Element Method (FEM). The cost and accuracy FEM scale with resolution underlying computational mesh. To balance speed meshes adaptive used, allocating more resources to critical parts geometry. Currently, practitioners often resort hand-crafted meshes, which extensive expert knowledge...