@incollection{ZachowWeiserHegeetal.2005, author = {Zachow, Stefan and Weiser, Martin and Hege, Hans-Christian and Deuflhard, Peter}, title = {Soft Tissue Prediction in Computer Assisted Maxillofacial Surgery Planning}, series = {Biomechanics Applied to Computer Assisted Surgery}, booktitle = {Biomechanics Applied to Computer Assisted Surgery}, editor = {Payan, Y.}, publisher = {Research Signpost}, pages = {277 -- 298}, year = {2005}, language = {en} } @incollection{ZachowWeiserDeuflhard2008, author = {Zachow, Stefan and Weiser, Martin and Deuflhard, Peter}, title = {Modellgest{\"u}tzte Operationsplanung in der Kopfchirurgie}, series = {Modellgest{\"u}tzte Therapie}, booktitle = {Modellgest{\"u}tzte Therapie}, editor = {Niederlag, Wolfgang and Lemke, Heinz and Meixensberger, J{\"u}rgen and Baumann, Michael}, publisher = {Health Academy}, pages = {140 -- 156}, year = {2008}, language = {en} } @misc{WustWeihrauchWeiseretal., author = {Wust, Peter and Weihrauch, Mirko and Weiser, Martin and Gellermann, Johanna and Eisenhardt, Steffen and Chobrok, Thorsten and Budach, Volker}, title = {Optimization of clinical radiofrequency hyperthermia by use of MR-thermography in a hybrid system}, series = {World Congress on Medical Physics and Biomedical Engineering, September 2009, Munich, Germany}, journal = {World Congress on Medical Physics and Biomedical Engineering, September 2009, Munich, Germany}, editor = {D{\"o}ssel, O. and Schlegel, W. and Magjarevic, R.}, publisher = {Springer}, pages = {174 -- 175}, language = {en} } @article{WilhelmsSeemannWeiseretal., author = {Wilhelms, Mathias and Seemann, Gunnar and Weiser, Martin and D{\"o}ssel, Olaf}, title = {Benchmarking Solvers of the Monodomain Equation in Cardiac Electrophysiological Modeling}, series = {Biomed. Engineer.}, volume = {55}, journal = {Biomed. Engineer.}, doi = {10.1515/BMT.2010.712}, pages = {99 -- 102}, language = {en} } @article{WeiserZachowDeuflhard2010, author = {Weiser, Martin and Zachow, Stefan and Deuflhard, Peter}, title = {Craniofacial Surgery Planning Based on Virtual Patient Models}, series = {it - Information Technology}, volume = {52}, journal = {it - Information Technology}, number = {5}, publisher = {Oldenbourg Verlagsgruppe}, doi = {10.1524/itit.2010.0600}, pages = {258 -- 263}, year = {2010}, language = {en} } @article{WeiserSchielaDeuflhard2005, author = {Weiser, Martin and Schiela, Anton and Deuflhard, Peter}, title = {Asymptotic Mesh Independence of Newton's Method Revisited}, series = {SIAM J. Num. Anal.}, volume = {42}, journal = {SIAM J. Num. Anal.}, number = {5}, pages = {1830 -- 1845}, year = {2005}, language = {en} } @article{WeiserSchiela2004, author = {Weiser, Martin and Schiela, Anton}, title = {Function space interior point methods for PDE constrained optimization}, series = {PAMM}, volume = {4}, journal = {PAMM}, number = {1}, pages = {43 -- 46}, year = {2004}, language = {en} } @inproceedings{WeiserScacchi, author = {Weiser, Martin and Scacchi, Simone}, title = {Spectral Deferred Correction methods for adaptive electro-mechanical coupling in cardiac simulation}, series = {G. Russo et al.(eds.) Progress in Industrial Mathematics at ECMI 2014}, booktitle = {G. Russo et al.(eds.) Progress in Industrial Mathematics at ECMI 2014}, publisher = {Springer}, doi = {10.1007/978-3-319-23413-7_42}, pages = {321 -- 328}, abstract = {We investigate spectral deferred correction (SDC) methods for time stepping and their interplay with spatio-temporal adaptivity, applied to the solution of the cardiac electro-mechanical coupling model. This model consists of the Monodomain equations, a reaction-diffusion system modeling the cardiac bioelectrical activity, coupled with a quasi-static mechanical model describing the contraction and relaxation of the cardiac muscle. The numerical approximation of the cardiac electro-mechanical coupling is a challenging multiphysics problem, because it exhibits very different spatial and temporal scales. Therefore, spatio-temporal adaptivity is a promising approach to reduce the computational complexity. SDC methods are simple iterative methods for solving collocation systems. We exploit their flexibility for combining them in various ways with spatio-temporal adaptivity. The accuracy and computational complexity of the resulting methods are studied on some numerical examples.}, language = {en} } @article{WeiserRoelligArndtetal., author = {Weiser, Martin and R{\"o}llig, Mathias and Arndt, Ralf and Erdmann, Bodo}, title = {Development and test of a numerical model for pulse thermography in civil engineering}, series = {Heat and Mass Transfer}, volume = {46}, journal = {Heat and Mass Transfer}, number = {11-12}, pages = {1419 -- 1428}, abstract = {Pulse thermography of concrete structures is used in civil engineering for detecting voids, honeycombing and delamination. The physical situation is readily modeled by Fourier's law. Despite the simplicity of the PDE structure, quantitatively realistic numerical 3D simulation faces two major obstacles. First, the short heating pulse induces a thin boundary layer at the heated surface which encapsulates all information and therefore has to be resolved faithfully. Even with adaptive mesh refinement techniques, obtaining useful accuracies requires an unsatisfactorily fine discretization. Second, bulk material parameters and boundary conditions are barely known exactly. We address both issues by a semi-analytic reformulation of the heat transport problem and by parameter identification. Numerical results are compared with measurements of test specimens.}, language = {en} } @article{WeiserGoetschel, author = {Weiser, Martin and G{\"o}tschel, Sebastian}, title = {State Trajectory Compression for Optimal Control with Parabolic PDEs}, series = {SIAM J. Sci. Comput.}, volume = {34}, journal = {SIAM J. Sci. Comput.}, number = {1}, doi = {10.1137/11082172X}, pages = {A161 -- A184}, abstract = {In optimal control problems with nonlinear time-dependent 3D PDEs, full 4D discretizations are usually prohibitive due to the storage requirement. For this reason gradient and quasi-Newton methods working on the reduced functional are often employed. The computation of the reduced gradient requires one solve of the state equation forward in time, and one backward solve of the adjoint equation. The state enters into the adjoint equation, again requiring the storage of a full 4D data set. We propose a lossy compression algorithm using an inexact but cheap predictor for the state data, with additional entropy coding of prediction errors. As the data is used inside a discretized, iterative algorithm, lossy coding maintaining an error bound is sufficient.}, language = {en} }