TY - CHAP A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian T1 - A nonlinear soft tissue model for craniofacial surgery simulations T2 - Proc. of Modeling and Simulation for Computer-aided Medicine and Surgery (MS4CMS Y1 - 2002 PB - INRIA CY - Paris, France ER - TY - CHAP A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian T1 - Biomechanical modeling of individual facial emotion expressions T2 - Proc. of Visualization, Imaging, and Image Processing (VIIP) Y1 - 2002 SP - 7 EP - 11 CY - Malaga, Spain ER - TY - CHAP A1 - Gladilin, Evgeny A1 - Zachow, Stefan A1 - Deuflhard, Peter A1 - Hege, Hans-Christian ED - Meiler, M. ED - Saupe, D. ED - Krugel, F. ED - Handels, H. ED - Lehmann, T. T1 - Biomechanisches Modell zur Abschätzung der individuellen Gesichtsmimik T2 - Proc.of Workshop Bildverarbeitung für die Medizin (BVM) Y1 - 2002 SP - 25 EP - 28 CY - Leipzig, Germany ER - TY - CHAP A1 - Zachow, Stefan A1 - Weiser, Martin A1 - Hege, Hans-Christian A1 - Deuflhard, Peter ED - Payan, Y. T1 - Soft Tissue Prediction in Computer Assisted Maxillofacial Surgery Planning T2 - Biomechanics Applied to Computer Assisted Surgery Y1 - 2005 SP - 277 EP - 298 PB - Research Signpost ER - TY - GEN A1 - Sagnol, Guillaume A1 - Hege, Hans-Christian A1 - Weiser, Martin T1 - Using sparse kernels to design computer experiments with tunable precision N2 - Statistical methods to design computer experiments usually rely on a Gaussian process (GP) surrogate model, and typically aim at selecting design points (combinations of algorithmic and model parameters) that minimize the average prediction variance, or maximize the prediction accuracy for the hyperparameters of the GP surrogate. In many applications, experiments have a tunable precision, in the sense that one software parameter controls the tradeoff between accuracy and computing time (e.g., mesh size in FEM simulations or number of Monte-Carlo samples). We formulate the problem of allocating a budget of computing time over a finite set of candidate points for the goals mentioned above. This is a continuous optimization problem, which is moreover convex whenever the tradeoff function accuracy vs. computing time is concave. On the other hand, using non-concave weight functions can help to identify sparse designs. In addition, using sparse kernel approximations drastically reduce the cost per iteration of the multiplicative weights updates that can be used to solve this problem. T3 - ZIB-Report - 16-33 KW - Optimal design of computer experiments KW - Sparse kernels KW - Gaussian Process Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-59605 SN - 1438-0064 ER -