@inproceedings {INPROC-2019-39,
   author = {Klaudius Scheufele and Shashank Subramanian and Andreas Mang and George Biros and Miriam Mehl},
   title = {{IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION}},
   booktitle = {SIAM Journal on Scientific Computing},
   publisher = {Springer},
   institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
   pages = {1--24},
   type = {Conference Paper},
   month = {July},
   year = {2019},
   keywords = {tumor progression inversion; biophysical model calibration; image registration; PDE constrained optimization; Picard iteration},
   language = {English},
   cr-category = {J.3 Life and Medical Sciences},
   ee = {https://arxiv.org/abs/1907.07774},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {We present a novel formulation for the calibration of a biophysical tumor
      growth model from a single-time snapshot, MRI scan of a glioblastoma patient.
      Tumor growth models are typically nonlinear parabolic partial differential
      equations (PDEs). Thus, we have to generate a second snapshot to be able to
      extract significant information from a single patient snapshot. We create this
      two-snapshot scenario as follows. We use an atlas (an average of several scans
      of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of
      the patient. Then, using the patient scan and the atlas, we combine
      image-registration algorithms and parameter estimation algorithms to achieve a
      better estimate of the healthy patient scan and the tumor growth parameters
      that are consistent with the data. Our scheme is based on our recent work
      (Scheufele et al, ``Biophysically constrained diffeomorphic image registration,
      Tumor growth, Atlas registration, Adjoint-based methods, Parallel algorithms'',
      CMAME, 2018), but apply a different and novel scheme where the tumor growth
      simulation in contrast to the previous work is executed in the patient brain
      domain and not in the atlas domain yielding more meaningful patient-specific
      results. As a basis, we use a PDE-constrained optimization framework. We derive
      a modified Picard-iteration-type solution strategy in which we alternate
      between registration and tumor parameter estimation in a new way. In addition,
      we consider an {\^a}„“1 sparsity constraint on the initial condition for the tumor
      and integrate it with the new joint inversion scheme. We solve the subproblems
      with a reduced-space, inexact Gauss-Newton-Krylov/quasi-Newton methods. We
      present results using real brain data with synthetic tumor data that show that
      the new scheme reconstructs the tumor parameters in a more accurate and
      reliable way compared to our earlier scheme.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2019-39&engl=1}
}

@article {ART-2019-06,
   author = {Miriam Mehl and Michael Lahnert},
   title = {{Adaptive grid implementation for parallel continuum mechanics methods in particle simulations}},
   journal = {The European Physical Journal Special Topics},
   editor = {Christian Holm and Thomas Ertl and Siegfried Schmauder and Johannes K{\"a}stner and Joachim Gross},
   publisher = {Springer Berlin Heidelberg},
   volume = {227},
   number = {14},
   pages = {1757--1778},
   type = {Article in Journal},
   month = {March},
   year = {2019},
   doi = {10.1140/epjst/e2019-800161-5},
   language = {German},
   cr-category = {G.0 Mathematics of Computing General},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {In this tutorial review paper, we present our minimally invasive approach for
      integrating dynamically adaptive tree-structured grids into existing simulation
      software that has been developed for regular Cartesian grids. We introduce
      different physical models that we target and that span a wide range of typical
      simulation characteristics -- from grid-based Lattice-Boltzmann, finite volume
      and finite difference discretized models to particle-based molecular dynamics
      models. We derive the respective typical data access requirements and
      extensions of the algorithms to adaptively refined grids along with possible
      grid adaptivity criteria. In addition, after introducing basics of
      tree-structured adaptively refined grids, we present the adaptive grid
      framework p4est and our enhancement of p4est in order to provide a grid and
      partitioning infrastructure that can easily be used in existing simulation
      codes. Finally, we explain how such a grid infrastructure can be integrated
      into regular grid codes in general in three major steps and how we integrated
      p4est in the soft matter simulation package ESPResSo in particular. A summary
      of results fro m previously published performance and scalability studies
      together with new results for more realistic coupled simulation scenarios shows
      the efficiency and validity of the resulting new version of ESPResSo.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2019-06&engl=1}
}

@article {ART-2019-05,
   author = {Steffen Hirschmann and Colin W. Glass and Dirk Pfl{\"u}ger},
   title = {{Enabling unstructured domain decompositions for inhomogeneous short-range molecular dynamics in ESPResSo}},
   journal = {The European Physical Journal Special Topics},
   publisher = {Springer Nature},
   volume = {227},
   number = {14},
   pages = {1779--1788},
   type = {Article in Journal},
   month = {March},
   year = {2019},
   issn = {1951-6401},
   doi = {10.1140/epjst/e2019-800159-0},
   language = {English},
   cr-category = {G.0 Mathematics of Computing General},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {In short-range molecular dynamics (MD) simulations, inhomogeneous particle
      distributions that dynamically change over time require flexible load-balancing
      methods to achieve good parallel efficiency. We have realized a general
      framework that can support different load-balancing methods and that can extend
      existing simulation packages in a minimally invasive way. This is a follow-up
      to recent work where we integrated it into the MD software ESPResSo to support
      load-balancing. We have realized a first partitioning strategy based on
      space-filling curves that can be used for efficient load-balanced multi-physics
      simulations. In this work we present a new graph-based partitioning strategy
      that leads to unstructured spatial domain decompositions and integrates well
      into the existing framework. We apply this to an inhomogeneous soot
      agglomeration scenario. For several load metrics, graph partitioning leads to
      better results than space-filling curves. The results indicate that the
      parallel performance for a given scenario requires a delicate combination of
      partitioning strategy and load metrics.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2019-05&engl=1}
}

@article {ART-2019-01,
   author = {Klaudius Scheufele and Andreas Mang and Amir Gholami and Christos Davatzikos and George Biros and Miriam Mehl},
   title = {{Coupling Brain-Tumor Biophysical Models and Diffeomorphic Image Registration}},
   journal = {Computer Methods in Applied Mechanics and Engineering},
   editor = {Elsevier},
   publisher = {Elsevier},
   pages = {1--34},
   type = {Article in Journal},
   month = {January},
   year = {2019},
   doi = {https://doi.org/10.1016/j.cma.2018.12.008},
   keywords = {biophysically constrained diffeomorphic image registration; tumor growth; atlas registration; adjoint-based methods; parallel algorithms},
   language = {English},
   cr-category = {G.1.6 Numerical Analysis Optimization,
                   G.1.8 Partial Differential Equations,
                   J.3 Life and Medical Sciences},
   ee = {https://arxiv.org/abs/1710.06420},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a
      framework for joint image registration and biophysical inversion and we apply
      it to analyse MR images of glioblastomas (primary brain tumors). We have two
      applications in mind. The first one is normal-to-abnormal image registration in
      the presence of tumor-induced topology differences. The second one is
      biophysical inversion based on single-time patient data. The underlying
      optimization problem is highly non-linear and non-convex and has not been
      solved before with a gradient-based approach. Given the segmentation of a
      normal brain MRI and the segmentation of a cancer patient MRI, we determine
      tumor growth parameters and a registration map so that if we ``grow a tumor''
      (using our tumor model) in the normal brain and then register it to the patient
      image, then the registration mismatch is as small as possible. This
      ``$\backslash$emph{coupled problem}'' two-way couples the biophysical inversion and the
      registration problem. In the image registration step we solve a
      large-deformation diffeomorphic registration problem parameterized by an
      Eulerian velocity field. In the biophysical inversion step we estimate
      parameters in a reaction-diffusion tumor growth model that is formulated as a
      partial differential equation (PDE). In SIBIA, we couple these two
      sub-components in an iterative manner. We first presented the components of
      SIBIA in ``Gholami et al, Framework for Scalable Biophysics-based Image
      Analysis, IEEE/ACM Proceedings of the SC2017'', in which we derived parallel
      distributed memory algorithms and software modules for the decoupled
      registration and biophysical inverse problems.
      
      In this paper, our contributions are the introduction of a PDE-constrained
      optimization formulation of the coupled problem, and the derivation of a Picard
      iterative solution scheme. We perform extensive tests to experimentally assess
      the performance of our method on synthetic and clinical datasets. We
      demonstrate the convergence of the SIBIA optimization solver in different usage
      scenarios. We demonstrate that using SIBIA, we can accurately solve the coupled
      problem in three dimensions (256^3 resolution) in a few minutes using 11
      dual-x86 nodes.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2019-01&engl=1}
}