Universität Stuttgart

Permanent URI for this communityhttps://elib.uni-stuttgart.de/handle/11682/1

Browse

Filters

2020 - 20243

Settings

Start date: 2020Institute: search.filters.UBSInstitut.Institut für Parallele und Verteilte SystemeSubject: search.filters.subject.620Subject: search.filters.subject.510

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Optimality principles in human point-to-manifold reaching accounting for muscle dynamics
    (2020) Wochner, Isabell; Driess, Danny; Zimmermann, Heiko; Häufle, Daniel F. B.; Toussaint, Marc; Schmitt, Syn
    Human arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.
  • Thumbnail Image
    ItemOpen Access
    Load-balancing for scalable simulations with large particle numbers
    (2021) Hirschmann, Steffen; Pflüger, Dirk (Prof. Dr.)
  • Thumbnail Image
    ItemOpen Access
    Knowledge-based modeling of simulation behavior for Bayesian optimization
    (2024) Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, Miriam
    Numerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.