@inproceedings {INPROC-2020-30,
   author = {Alireza Naseri and Amin Totounferoush and Ignacio Gonzales and Miriam Mehl and Carlos P{\'e}rez-Segarra},
   title = {{A scalable framework for the partitioned solution of fluid–structure interaction problems}},
   booktitle = {Computational Mechanics},
   publisher = {Springer},
   institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
   type = {Conference Paper},
   month = {May},
   year = {2020},
   isbn = {10.1007/s00466-020-01860-y},
   keywords = {Mehl, Miriam; P{\'e}rez-Segarra, Carlos},
   language = {English},
   cr-category = {G.1.8 Partial Differential Equations,
                   J.2 Physical Sciences and Engineering,
                   J.3 Life and Medical Sciences},
   ee = {https://link.springer.com/article/10.1007/s00466-020-01860-y},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {In this work, we present a scalable and efficient parallel solver for the
      partitioned solution of fluid–structure interaction problems through multi-code
      coupling. Two instances of an in-house parallel software, TermoFluids, are used
      to solve the fluid and the structural sub-problems, coupled together on the
      interface via the preCICE coupling library. For fluid flow, the Arbitrary
      Lagrangian–Eulerian form of the Navier–Stokes equations is solved on an
      unstructured conforming grid using a second-order finite-volume discretization.
      A parallel dynamic mesh method for unstructured meshes is used to track the
      moving boundary. For the structural problem, the nonlinear elastodynamics
      equations are solved on an unstructured grid using a second-order finite-volume
      method. A semi-implicit FSI coupling method is used which segregates the fluid
      pressure term and couples it strongly to the structure, while the remaining
      fluid terms and the geometrical nonlinearities are only loosely coupled. A
      robust and advanced multi-vector quasi-Newton method is used for the coupling
      iterations between the solvers. Both the fluid and the structural solver use
      distributed-memory parallelism. The intra-solver communication required for
      data update in the solution process is carried out using non-blocking
      point-to-point communicators. The inter-code communication is fully parallel
      and point-to-point, avoiding any central communication unit. Inside each
      single-physics solver, the load is balanced by dividing the computational
      domain into fairly equal blocks for each process. Additionally, a load
      balancing model is used at the inter-code level to minimize the overall idle
      time of the processes. Two practical test cases in the context of hemodynamics
      are studied, demonstrating the accuracy and computational efficiency of the
      coupled solver. Strong scalability test results show a parallel efficiency of
      83\% on 10,080 CPU cores.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-30&engl=1}
}

@inproceedings {INPROC-2020-16,
   author = {Steffen Hirschmann and Andreas Kronenburg and Colin W. Glass and Dirk Pfl{\"u}ger},
   title = {{Load-Balancing for Large-Scale Soot Particle Agglomeration Simulations}},
   booktitle = {Parallel Computing: Technology Trends},
   editor = {Ian Foster and Gerhard R. Joubert and Ludek Kucera and Wolfgang E. Nagel and Frans Peters},
   publisher = {IOS Press},
   institution = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Germany},
   series = {Advances in Parallel Computing},
   volume = {36},
   pages = {147--156},
   type = {Conference Paper},
   month = {March},
   year = {2020},
   doi = {10.3233/APC200035},
   language = {English},
   cr-category = {G.0 Mathematics of Computing General},
   ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2020-16/INPROC-2020-16.pdf},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {In this work, we combine several previous efforts to simulate a large-scale
      soot particle agglomeration with a dynamic, multi-scale turbulent background
      flow field. We build upon previous simulations which include 3.2 million
      particles and implement load-balancing into the used simulation software as
      well as tests of the load-balancing mechanisms on this scenario. We increase
      the simulation to 109.85 million particles, superpose a dynamically changing
      multi-scale background flow field and use our software enhancements to the
      molecular dynamics software ESPResSo to simulate this on a Cray XC40
      supercomputer. To verify that our setup reproduces essential physics we scale
      the influence of the flow field down to make the scenario mostly homogeneous on
      the subdomain scale. Finally, we show that even on the homogeneous version of
      this soot particle agglomeration simulation, load-balancing still pays off.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2020-16&engl=1}
}

@article {ART-2020-09,
   author = {Alireza Naseri and Amin Totounferoush and Ignacio Gonzales and Miriam Mehl and Carlos David Perez-Segarra},
   title = {{A scalable framework for the partitioned solution of fluid–structure interaction problems}},
   journal = {Computational Mechanics},
   publisher = {Springer Verlag},
   volume = {66},
   pages = {471--489},
   type = {Article in Journal},
   month = {May},
   year = {2020},
   isbn = {https://doi.org/10.1007/s00466-020-01860-y},
   keywords = {Fluid-Structure Interaction; Partitioned Method; Multi-Code Coupling; Scalability; HPC},
   language = {English},
   cr-category = {J.2 Physical Sciences and Engineering,
                   J.3 Life and Medical Sciences,
                   I.6.3 Simulation and Modeling Applications},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {In this work, we present a scalable and efficient parallel solver for the
      partitioned solution of fluid–structure interaction problems through multi-code
      coupling. Two instances of an in-house parallel software, TermoFluids, are used
      to solve the fluid and the structural sub-problems, coupled together on the
      interface via the preCICE coupling library. For fluid flow, the Arbitrary
      Lagrangian–Eulerian form of the Navier–Stokes equations is solved on an
      unstructured conforming grid using a second-order finite-volume discretization.
      A parallel dynamic mesh method for unstructured meshes is used to track the
      moving boundary. For the structural problem, the nonlinear elastodynamics
      equations are solved on an unstructured grid using a second-order finite-volume
      method. A semi-implicit FSI coupling method is used which segregates the fluid
      pressure term and couples it strongly to the structure, while the remaining
      fluid terms and the geometrical nonlinearities are only loosely coupled. A
      robust and advanced multi-vector quasi-Newton method is used for the coupling
      iterations between the solvers. Both the fluid and the structural solver use
      distributed-memory parallelism. The intra-solver communication required for
      data update in the solution process is carried out using non-blocking
      point-to-point communicators. The inter-code communication is fully parallel
      and point-to-point, avoiding any central communication unit. Inside each
      single-physics solver, the load is balanced by dividing the computational
      domain into fairly equal blocks for each process. Additionally, a load
      balancing model is used at the inter-code level to minimize the overall idle
      time of the processes. Two practical test cases in the context of hemodynamics
      are studied, demonstrating the accuracy and computational efficiency of the
      coupled solver. Strong scalability test results show a parallel efficiency of
      83\% on 10,080 CPU cores.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-09&engl=1}
}

@article {ART-2020-08,
   author = {Shashank Subramanian and Klaudius Scheufele and Miriam Mehl and George Biros},
   title = {{Where did the tumor start? An inverse solver with sparse localization for tumor growth models}},
   journal = {Inverse Problems},
   publisher = {IOP Publisher},
   volume = {36},
   number = {4},
   type = {Article in Journal},
   month = {February},
   year = {2020},
   isbn = {10.1088/1361-6420/ab649c},
   language = {English},
   cr-category = {G.1.2 Numerical Analysis Approximation,
                   G.1.6 Numerical Analysis Optimization,
                   G.1.8 Partial Differential Equations,
                   I.4 Image Processing and Computer Vision,
                   I.6.8 Types of Simulation,
                   J.3 Life and Medical Sciences},
   ee = {https://iopscience.iop.org/article/10.1088/1361-6420/ab649c,
      https://arxiv.org/abs/1907.06564},
   contact = {miriam.mehl@ipvs.uni-stuttgart.de},
   department = {University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems},
   abstract = {We present a numerical scheme for solving an inverse problem for parameter
      estimation in tumor growth models for glioblastomas, a form of aggressive
      primary brain tumor. The growth model is a reaction–diffusion partial
      differential equation (PDE) for the tumor concentration. We use a
      PDE-constrained optimization formulation for the inverse problem. The unknown
      parameters are the reaction coefficient (proliferation), the diffusion
      coefficient (infiltration), and the initial condition field for the tumor PDE.
      Segmentation of magnetic resonance imaging (MRI) scans drive the inverse
      problem where segmented tumor regions serve as partial observations of the
      tumor concentration. Like most cases in clinical practice, we use data from a
      single time snapshot. Moreover, the precise time relative to the initiation of
      the tumor is unknown, which poses an additional difficulty for inversion. We
      perform a frozen-coefficient spectral analysis and show that the inverse
      problem is severely ill-posed. We introduce a biophysically motivated
      regularization on the structure and magnitude of the tumor initial condition.
      In particular, we assume that the tumor starts at a few locations (enforced
      with a sparsity constraint on the initial condition of the tumor) and that the
      initial condition magnitude in the maximum norm is equal to one. We solve the
      resulting optimization problem using an inexact quasi-Newton method combined
      with a compressive sampling algorithm for the sparsity constraint. Our
      implementation uses PETSc and AccFFT libraries. We conduct numerical
      experiments on synthetic and clinical images to highlight the improved
      performance of our solver over a previously existing solver that uses standard
      two-norm regularization for the calibration parameters. The existing solver is
      unable to localize the initial condition. Our new solver can localize the
      initial condition and recover infiltration and proliferation. In clinical
      datasets (for which the ground truth is unknown), our solver results in
      qualitatively different solutions compared to the two-norm regularized solver.},
   url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-08&engl=1}
}