Zobrazeno 1 - 10
of 1 106
pro vyhledávání: '"A. Freymuth"'
Finding efficient routes for data packets is an essential task in computer networking. The optimal routes depend greatly on the current network topology, state and traffic demand, and they can change within milliseconds. Reinforcement Learning can he
Externí odkaz:
http://arxiv.org/abs/2410.10377
Probabilistic State Space Models (SSMs) are essential for Reinforcement Learning (RL) from high-dimensional, partial information as they provide concise representations for control. Yet, they lack the computational efficiency of their recent determin
Externí odkaz:
http://arxiv.org/abs/2406.15131
Autor:
Freymuth, Niklas, Dahlinger, Philipp, Würth, Tobias, Becker, Philipp, Taranovic, Aleksandar, Grönheim, Onno, Kärger, Luise, Neumann, Gerhard
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
Externí odkaz:
http://arxiv.org/abs/2406.14161
Autor:
Freymuth, Niklas, Dahlinger, Philipp, Würth, Tobias, Reisch, Simon, Kärger, Luise, Neumann, Gerhard
Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element Method (FEM) are widely used. However, the FEM becomes computationally
Externí odkaz:
http://arxiv.org/abs/2406.08440
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 development of part design, material system and manufactur
Externí odkaz:
http://arxiv.org/abs/2402.10681
Autor:
Scheikl, Paul Maria, Schreiber, Nicolas, Haas, Christoph, Freymuth, Niklas, Neumann, Gerhard, Lioutikov, Rudolf, Mathis-Ullrich, Franziska
Publikováno v:
IEEE Robotics and Automation Letters 9 (2024) 5338-5345
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions. To this end, we introduce Movement Primitive Diffusion (MPD), a novel method for i
Externí odkaz:
http://arxiv.org/abs/2312.10008
Simulating dynamic physical interactions is a critical challenge across multiple scientific domains, with applications ranging from robotics to material science. For mesh-based simulations, Graph Network Simulators (GNSs) pose an efficient alternativ
Externí odkaz:
http://arxiv.org/abs/2311.05256
Autor:
Freymuth, Niklas, Dahlinger, Philipp, Würth, Tobias, Reisch, Simon, Kärger, Luise, Neumann, Gerhard
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
Externí odkaz:
http://arxiv.org/abs/2304.00818
Autor:
Linkerhägner, Jonas, Freymuth, Niklas, Scheikl, Paul Maria, Mathis-Ullrich, Franziska, Neumann, Gerhard
Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations whi
Externí odkaz:
http://arxiv.org/abs/2302.11864
Autor:
Freymuth, Niklas, Schreiber, Nicolas, Becker, Philipp, Taranovic, Aleksandar, Neumann, Gerhard
Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Thus, they can easily generalize and adapt to new and changing environments. Current Imitation Learning algorithm
Externí odkaz:
http://arxiv.org/abs/2210.08121