Dissertations

Dissertations in PUMA

  1. 2024

    1. Alkämper, M.: A moving mesh finite volume method for hyperbolic interface problems, (2024). https://doi.org/10.18419/opus-14870.
    2. Buchfink, P.: Structure-preserving model reduction on subspaces and manifolds, (2024). https://doi.org/10.18419/opus-14563.
    3. Demir, S.Ö.: Learning-based control and localization of magnetic soft millirobots, (2024). https://doi.org/10.18419/opus-14687.
    4. Díaz Carral, Á.: Physics-driven machine learning : from biomolecules to crystals, (2024). https://doi.org/10.18419/opus-15216.
    5. Hammer, M.: Biophysical validity of reduced soft tissue modelling in neuro-musculoskeletal simulations, (2024). https://doi.org/10.18419/opus-14829.
    6. Heitkämper, J.: Computational investigation of catalytic reaction mechanisms, (2024). https://doi.org/10.18419/opus-14580.
    7. Keim, J.: Van der Waals-Korteweg-type models for the simulation of compressible multiphase flow, (2024).
    8. Kim, H.: Development of a bioinspired multimodal mobile robot platform, (2024). https://doi.org/10.18419/opus-14646.
    9. Kurz, M.: Machine learning methods for modeling turbulence in Large Eddy Simulations, (2024).
    10. Lipp, M.G.: Capturing local details in fluid-flow simulations : options, challenges and applications using marker-and-cell schemes, (2024). https://doi.org/10.18419/opus-14910.
    11. Lohrmann, C.: Motile bacteria in complex environments, (2024). https://doi.org/10.18419/opus-14822.
    12. Pollinger, T.: Stable and mass-conserving high-dimensional simulations with the sparse grid combination technique for full HPC systems and beyond, (2024). https://doi.org/10.18419/opus-14210.
    13. Schnee, P.: Molecular dynamics simulations of the substrate- and product specificity and mechanism of DNA- and protein lysine methyltransferases, (2024). https://doi.org/10.18419/opus-14472.
    14. Weder, B.: Workflow-basierte Modellierung, Ausführung und Überwachung hybrider Quantenanwendungen, (2024). https://doi.org/10.18419/opus-15072.

  2. 2023

    1. Born, D.: Machine-learning techniques for geometry optimization, (2023). https://doi.org/10.18419/opus-13267.
    2. Burbulla, S.: Mixed-dimensional modeling of flow in porous media, (2023). https://doi.org/10.18419/opus-12712.
    3. Davis, K.: Computational methods for partitioned simulation coupling : applications in multi-physics simulations and energy infrastructure optimisation, (2023). https://doi.org/10.18419/opus-13140.
    4. Eller, J.: PC-SAFT density functional theory in 3 dimensions : adsorption in ordered porous media and solvation free energies in non-polar solvents, (2023). https://doi.org/10.18419/opus-13469.
    5. Fischer, M.: Dynamic properties of fluids from molecular simulations and entropy scaling, (2023). https://doi.org/10.18419/opus-12905.
    6. Guttà, C.: Prognostication and prediction of cancer patient outcomes using AI-based classifiers, (2023). https://doi.org/10.18419/opus-13347.
    7. Hahn, M.: Transparent data exchange in service choreographies : an eScience perspective, (2023). https://doi.org/10.18419/opus-13034.
    8. Holzmüller, D.: Regression from linear models to neural networks : double descent, active learning, and sampling, (2023). https://doi.org/10.18419/opus-13470.
    9. Jensch, A.: Methodological concepts for data-integrated modeling of biological systems with applications in cancer biology, (2023). https://doi.org/10.18419/opus-12907.
    10. Kempter, F.: Validierungsansätze von aktiven Menschmodellen unter Einbeziehung menschlicher Variabilität in Experiment und Simulation, (2023).
    11. Kraus, H.: Atomistic simulation of fluid structure and diffusion in functionalized mesoporous silica, (2023). https://doi.org/10.18419/opus-13467.
    12. Lambers, L.: Multiscale and multiphase modeling and numerical simulation of function-perfusion processes in the liver, (2023). https://doi.org/10.18419/opus-13042.
    13. Lee, Y.: 3D-printed stimuli-responsive soft microrobots, (2023). https://doi.org/10.18419/opus-13822.
    14. Praditia, T.: Physics-informed neural networks for learning dynamic, distributed and uncertain systems, (2023). https://doi.org/10.18419/opus-13229.
    15. Steinhausen, C.: Investigation of macroscopic nearcritical fluid phenomena by applying laser-induced thermal acoustics, (2023).
    16. Veyskarami, M.: Coupled free-flow-porous media flow processes including drop formation, (2023). https://doi.org/10.18419/opus-13894.
    17. Weber, D.: Empirical assessment and improvement of ubiquitous notifications, (2023). https://doi.org/10.18419/opus-15152.
    18. Wenzel, T.: Deep and greedy kernel methods : algorithms, analysis and applications, (2023). https://doi.org/10.18419/opus-14435.
    19. Wochner, I.: The benefit of muscle-actuated systems : internal mechanics, optimization and learning, (2023). https://doi.org/10.18419/opus-13849.
    20. Wolff, L. von: The phase field approach for reactive fluid-solid interfaces : modeling and homogenization, (2023). https://doi.org/10.18419/opus-12701.

  3. 2022

    1. Brencher, L.: Analysis of hyperbolic conservation laws with random discontinuous flux functions and their efficient simulation, (2022). https://doi.org/10.18419/opus-13566.
    2. Eggenweiler, E.: Interface conditions for arbitrary flows in Stokes-Darcy systems : derivation, analysis and validation, (2022). https://doi.org/10.18419/opus-12573.
    3. Holicki, T.: A complete analysis and design framework for linear impulsive and related hybrid systems, (2022). https://doi.org/10.18419/opus-12158.
    4. Huptych, M.: Online-Bahnplanung für mehrere Flugroboter in veränderlicher Umgebung mithilfe der Kurvenflussmethode, (2022). https://doi.org/10.18419/opus-12143.
    5. Merkle, R.: Subordinated fields and random elliptic partial differential equations, (2022). https://doi.org/10.18419/opus-12704.
    6. Michalkowski, C.: Modeling water transport at the interface between porous GDL and gas distributor of a PEM fuel cell cathode, (2022). https://doi.org/10.18419/opus-12106.
    7. Poljanšek, T.: Realität und Wirklichkeit : zur Ontologie geteilter Welten, (2022). https://doi.org/10.14361/9783839462409.
    8. Schuldt, R.: Computational analysis of periodic systems for covalent organic frameworks and molecules in high electric fields, (2022). https://doi.org/10.18419/opus-12252.
    9. Schäfer Rodrigues Silva, A.: Quantifying and visualizing model similarities for multi-model methods, (2022). https://doi.org/10.18419/opus-12399.
    10. Thomaseth, C.: A statistical framework to optimize experimental design for inference problems in systems biology based on normalized data, (2022). https://doi.org/10.18419/opus-12050.
    11. Totounferoush, A.: Data-integrated methods for performance improvement of massively parallel coupled simulations, (2022). https://doi.org/10.18419/opus-12375.
    12. Walter, J.R.: Über die Regelung muskelgetriebener Systeme : ein hierarchischer und geometriebasierter Ansatz, (2022). https://doi.org/10.18419/opus-12577.
    13. Weinhardt, F.: Porosity and permeability alterations in processes of biomineralization in porous media - microfluidic investigations and their interpretation, (2022). https://doi.org/10.18419/opus-12822.
    14. Wittwar, D.: Approximation with matrix-valued kernels and highly effective error estimators for reduced basis approximations, (2022). https://doi.org/10.18419/opus-12526.
    15. Zaverkin, V.: Investigation of chemical reactivity by machine-learning techniques, (2022). https://doi.org/10.18419/opus-12182.
    16. Zeman, J.: Dielectric effects in complex fluids, (2022). https://doi.org/10.18419/opus-12226.

  4. 2021

    1. Becker, B.: Development of efficient multiscale multiphysics models accounting for reversible flow at various subsurface energy storage sites, (2021). https://doi.org/10.18419/opus-11753.
    2. Becker, M.: Numerical simulation of fracture in high-velocity impact, (2021). https://doi.org/10.18419/opus-11547.
    3. Bleiler, C.: Continuum-mechanical modelling across scales : homogenisation methods and their application to microstructurally-based skeletal muscle modelling, (2021). https://doi.org/10.18419/opus-11959.
    4. Elagroudy, P.: Designing ubiquitous-computing systems for memory alterations, (2021). https://doi.org/10.18419/opus-12472.
    5. Föll, R.: Sparse deep gaussian process approximation and application of dynamic system identification, (2021).
    6. Gebhardt, J.: Biomolecular force fields probed by free energies of binding and solvation, (2021). https://doi.org/10.18419/opus-11671.
    7. Hamann, D.: Sensitivity analysis of the stability of machining systems, (2021).
    8. Happach, R.M.: System-Dynamics-Modelle als Erweiterung der Methoden der Investitionsrechnung für Stromspeicher im deutschen Elektrizitätsmarkt, (2021).
    9. Hilder, B.: Invasion phenomena in pattern-forming systems admitting a conservation law structure, (2021). https://doi.org/10.18419/opus-11552.
    10. Hirschmann, S.: Load-balancing for scalable simulations with large particle numbers, (2021). https://doi.org/10.18419/opus-11796.
    11. Hofmann, A.: Ansätze zur Analyse und Regelung unsicherheitsbehafteter Systeme mit Methoden der Möglichkeitstheorie, (2021).
    12. Kobayashi, T.: Tuning the properties and microstructuring of ionic liquid mixtures at surfaces through atomistic modeling, (2021). https://doi.org/10.18419/opus-11921.
    13. Kunc, O.: Finite strain hyperelastic multiscale homogenization via projection, efficient sampling and concentric interpolation, (2021). https://doi.org/10.18419/opus-12042.
    14. Linsenmayer, S.: Time- and event-triggered stabilization of linear networked control systems based on novel communication models, (2021).
    15. Magiera, J.: A molecular-continuum multiscale solver for liquid-vapor flow : modeling and numerical simulation, (2021). https://doi.org/10.18419/opus-11797.
    16. Maier, B.: Scalable biophysical simulations of the neuromuscular system, (2021). https://doi.org/10.18419/opus-11798.
    17. Narayanan Krishnamoorthy, A.: Modeling of complex electrolytes : a numerical simulation study, (2021). https://doi.org/10.18419/opus-11465.
    18. Ostrowski, L.: Compressible multi-component and multi-phase flows : interfaces and asymptotic regimes, (2021). https://doi.org/10.18419/opus-11811.
    19. Rehme, M.F.: B-splines on sparse grids for uncertainty quantification, (2021). https://doi.org/10.18419/opus-11754.
    20. Rehner, P.: Interfacial properties using classical density functional theory : curved interfaces and surfactants, (2021). https://doi.org/10.18419/opus-11914.
    21. Reuschen, S.: Bayesian inversion and model selection of heterogeneities in geostatistical subsurface modeling, (2021). https://doi.org/10.18419/opus-12013.
    22. Rörich, A.: A Bayesian approach to parameter reconstruction from surface electromyographic signals, (2021). https://doi.org/10.18419/opus-11690.
    23. Schirwon, M.: Efficient simulation of challenging PDE problems on CPU and GPU clusters, (2021). https://doi.org/10.18419/opus-11521.
    24. Seitz, G.: Modeling fixed-bed reactors for thermochemical heat storage with the reaction system CaO/Ca(OH)2, (2021). https://doi.org/10.18419/opus-11522.
    25. Sridhar, V.: Light-driven microswimmers and their applications, (2021). https://doi.org/10.18419/opus-11889.
    26. Voit, A.: Designing smart home appliances displaying non-urgent everyday information, (2021). https://doi.org/10.18419/opus-11678.
    27. Yunusa, M.: Interfacial mechanics and liquid crystal structure of liquid gallium, (2021). https://doi.org/10.18419/opus-12015.

  5. 2020

    1. Asgharzadeh, P.: Image-based analysis of biological network structures using machine learning and continuum mechanics, (2020). https://doi.org/10.18419/opus-11154.
    2. Baz, J.: On the prediction of thermodynamic properties by atomistic simulation : from vapor-liquid equilibrium of alcohols to self-assembly in mixed solvents, (2020). https://doi.org/10.18419/opus-10984.
    3. Cooper, A.M.: Accurate force- and Hessian predictions from neural network potentials, (2020). https://doi.org/10.18419/opus-11106.
    4. Gläser, D.: Discrete fracture modeling of multi-phase flow and deformation in fractured poroelastic media, (2020). https://doi.org/10.18419/opus-11040.
    5. Halter, W.: Modeling, analysis and design of genetic circuits for control and optimization inside cells, (2020).
    6. Hessenthaler, A.: Multilevel convergence analysis : parallel-in-time integration for fluid-structure interaction problems with applications in cardiac flow modeling, (2020). https://doi.org/10.18419/opus-11260.
    7. Imig, D.: Individual-based modeling of TRAIL-induced apoptosis in cancer cell populations, (2020).
    8. Koch, T.: Mixed-dimension models for flow and transport processes in porous media with embedded tubular network systems, (2020). https://doi.org/10.18419/opus-10975.
    9. Kuritz, K.: Analysis and control of cellular ensembles : exploiting dimensionality reduction in single-cell data and models, (2020).
    10. Köhler, P.N.: Distributed economic model predictive control: Cost-efficient operation of interconnected systems, (2020).
    11. Markthaler, D.: Disentangling force field and sampling issues in biomolecular systems, (2020). https://doi.org/10.18419/opus-11458.
    12. Mordhorst, M.: Towards a fast and stable dynamic skeletal muscle model, (2020). https://doi.org/10.18419/opus-11159.
    13. Poguntke, R.: Understanding stress responses related to digital technologies, (2020). https://doi.org/10.18419/opus-11015.
    14. Rambausek, M.: Magneto-electro-elasticity of soft bodies across scales, (2020). https://doi.org/10.18419/opus-11042.
    15. Schmidt-Scheele, R.: The plausibility of future scenarios : conceptualising an unexplored criterion in scenario planning, (2020). https://doi.org/10.14361/9783839453193.
    16. Weishaupt, K.: Model concepts for coupling free flow with porous medium flow at the pore-network scale : from single-phase flow to compositional non-isothermal two-phase flow, (2020). https://doi.org/10.18419/opus-10932.

  6. 2019

    1. Alkämper, M.: Mesh refinement for parallel-adaptive FEM : theory and implementation, (2019). https://doi.org/10.18419/opus-10568.
    2. Bidier, S.: From particle mechanics to micromorphic continua, (2019). https://doi.org/10.18419/opus-10804.
    3. Burkhardt, M.: Model-based feed-forward control for mechatronic systems with structural elasticity, (2019).
    4. Kleinbach, C.G.: Simulation of occupant kinematics using active human body models, (2019).
    5. Lahnert, M.: Adaptive grid implementation for parallel continuum mechanics methods in particle simulations, (2019). https://doi.org/10.18419/opus-10836.
    6. Le, H.V.: Hand-and-finger-awareness for mobile touch Interaction using deep learning, (2019). https://doi.org/10.18419/opus-10555.
    7. Lindner, F.: Data transfer in partitioned multi-physics simulations : interpolation & communication, (2019). https://doi.org/10.18419/opus-10581.
    8. Link, K.: Multiscale modeling and simulation of transport processes and electrochemical reactions in multimaterial porous electrodes, (2019).
    9. Lorenzen, M.: Predictive control under uncertainty : from conceptual aspects to computational approaches, (2019).
    10. Luo, C.: A phase-field model embedded in the theory of porous media with application to hydraulic fracturing, (2019). https://doi.org/10.18419/opus-10355.
    11. Mayer, S.: Finger orientation as an additional input dimension for touchscreens, (2019). https://doi.org/10.18419/opus-10397.
    12. Meyer, F.: Quantification of uncertainties in compressible flows, (2019). https://doi.org/10.18419/opus-10751.
    13. Most, S.C.: Analysis and simulation of anomalous transport in porous media, (2019). https://doi.org/10.18419/opus-10494.
    14. Reitzle, M.: A framework for the direct numerical simulation of phase change processes of water at low temperature and pressure, (2019).
    15. Schneider, M.: Nonlinear finite volume schemes for complex flow processes and challenging grids, (2019). https://doi.org/10.18419/opus-10416.
    16. Tempel, P.: Dynamics of cable-driven parallel robots with elastic and flexible, time-varying length cables, (2019). https://doi.org/10.18419/opus-10818.
    17. Valentin, J.: B-splines for sparse grids : algorithms and application to higher-dimensional optimization, (2019). https://doi.org/10.18419/opus-10504.
    18. Vallicotti, D.: Magneto-electro-mechanical coupling phenomena across multiple length scales : variational framework and stability analysis, (2019). https://doi.org/10.18419/opus-10427.
    19. Waibel, C.: Development of a polarizable transferable force field for vapor-liquid equilibria calculations, (2019). https://doi.org/10.18419/opus-10612.

  7. 2018

    1. Abdelrahman, Y.: Thermal imaging for amplifying human perception, (2018).
    2. Breitsprecher, K.: Simulation studies on electrodes and electrolytes for electric double layer capacitors, (2018). https://doi.org/10.18419/opus-10223.
    3. Gilbergs, H.: Identifikation statischer und dynamischer Veränderungen in optischen Systemen aus Wellenfrontmessungen, (2018).
    4. Heene, M.: A massively parallel combination technique for the solution of high-dimensional PDEs, (2018). https://doi.org/10.18419/opus-9893.
    5. Kane, B.: Adaptive higher order discontinuous Galerkin methods for porous-media multi-phase flow with strong heterogeneities, (2018). https://doi.org/10.18419/opus-9863.
    6. Köppel, M.: Flow in heterogeneous porous media : fractures and uncertainty quantification, (2018).
    7. Lischke, L.: Interacting with large high-resolution display workplaces, (2018). https://doi.org/10.18419/opus-10121.
    8. Meisner, J.: Theoretical investigations of atom tunneling in the interstellar medium, (2018). https://doi.org/10.18419/opus-9841.
    9. Paul, D.: Understanding the mechanisms of robustness in intracellular protein signalling cascades and gene expression, (2018). https://doi.org/10.18419/opus-10506.
    10. Scheufele, K.: Coupling schemes and inexact Newton for multi-physics and coupled optimization problems, (2018).
    11. Schmidt, A.: Feedback control for parametric partial differential equations using reduced basis surrogate models, (2018).
    12. Schöll, A.: Efficient fault tolerance for selected scientific computing algorithms on heterogeneous and approximate computer architectures, (2018). https://doi.org/10.18419/opus-9951.
    13. Stöhr, D.: Characterising heterogeneous TRAIL responsiveness and overcoming TRAIL resistance in multicellular tumour spheroids, (2018). https://doi.org/10.18419/opus-10295.
    14. Wu, J.: Distributed H∞ state estimation with applications to multi-agent coordination, (2018).

  8. 2017

    1. Adhikari, B.: Electronic, adsorption, and transport properties of diamondoid-based complexes, (2017). https://doi.org/10.18419/opus-9136.
    2. Bayer, F.A.: Performance and constraint satisfaction in robust economic model predictive control, (2017).
    3. Brunner, F.D.: Set-theoretic approaches to the aperiodic control of linear systems, (2017).
    4. Dingler, T.: Cognition-aware systems to support information intake and learning, (2017). https://doi.org/10.18419/opus-9461.
    5. Feller, C.: Relaxed barrier function based model predictive control : theory and algorithms, (2017).
    6. Fetzer, M.: From classical absolute stability tests towards a comprehensive robustness analysis, (2017). https://doi.org/10.18419/opus-9726.
    7. Franzelin, F.: Data-driven uncertainty quantification for large-scale simulations, (2017).
    8. Geissen, E.-M.: A statistical and mechanistic, model-based analysis of spindle assembly checkpoint signalling, (2017). https://doi.org/10.18419/opus-9845.
    9. Greis, M.: A systematic exploration of uncertainty in interactive systems, (2017). https://doi.org/10.18419/opus-9753.
    10. Grüninger, C.: Numerical coupling of Navier-Stokes and Darcy flow for soil-water evaporation, (2017). https://doi.org/10.18419/opus-9657.
    11. Hoher, S.: Ein gekoppeltes Materialflussmodell zur durchgängigen Entwicklungsunterstützung von Materialflusssteuerungen, (2017). https://doi.org/10.18419/opus-9309.
    12. Hopp-Hirschler, M.: Modeling of porous polymer membrane formation, (2017). https://doi.org/10.18419/opus-9462.
    13. Karch, G.K.: Visualization of two-phase flow dynamics : techniques for droplet interactions, interfaces, and material transport, (2017). https://doi.org/10.18419/opus-9684.
    14. Martini, I.: Reduced basis approximation for heterogeneous domain decomposition problems, (2017).
    15. Mauthe, S.A.: Variational multiphysics modeling of diffusion in elastic solids and hydraulic fracturing in porous media, (2017). https://doi.org/10.18419/opus-9321.
    16. Pfleging, B.: Automotive user interfaces for the support of non-driving-related activities, (2017). https://doi.org/10.18419/opus-9090.
    17. Reimann, P.: Data provisioning in simulation workflows, (2017). https://doi.org/10.18419/opus-9005.
    18. Samanta, P.K.: Excitation energies and response properties of molecules using internally contracted multireference coupled cluster methods, (2017). https://doi.org/10.18419/opus-9645.
    19. Schulte, K.: Modelling of the initial ice growth in a supercooled liquid droplet, (2017).
    20. Sinsbeck, M.: Uncertainty quantification for expensive simulations : optimal surrogate modeling under time constraints, (2017). https://doi.org/10.18419/opus-9206.
    21. Sivaraman, G.: Electronic transport properties of DNA sensing nanopores : insight from quantum mechanical simulations, (2017). https://doi.org/10.18419/opus-9179.
    22. Stühler, S.: Zur Modellierung von Bruchvorgängen mit verformbaren tetraedrischen Partikeln, (2017).

  9. 2016

    1. Breindl, C.: Identification, analysis and control of discrete and continuous models of gene regulation networks, (2016).
    2. Broy, N.: Stereoscopic 3D user interfaces : exploring the potentials and risks of 3D displays in cars, (2016). https://doi.org/10.18419/opus-8851.
    3. Fischer, A.: Untersuchung von Regelungskonzepten für unsicherheitsbehaftete Zerspanprozesse mit elastischen Werkstücken, (2016).
    4. Funk, M.: Augmented reality at the workplace : a context-aware assistive system using in-situ projection, (2016). https://doi.org/10.18419/opus-8997.
    5. Kosow, H.: The best of both worlds? : an exploratory study on forms and effects of new qualitative-quantitative scenario methodologies, (2016). https://doi.org/10.18419/opus-9015.
    6. Minina, E.: Entropic segregation of polymers under confinement, (2016). https://doi.org/10.18419/opus-8787.
    7. Montenbruck, J.M.: Constructive approaches to submanifold stabilization, (2016).
    8. Neusser, J.: Numerical approximation of two-phase flows with and without phase transition, (2016).
    9. Rybak, I.: Mathematical modeling of coupled free flow and porous medium systems, (2016). https://doi.org/10.18419/opus-8978.
    10. Schneegass, S.: Enriching mobile interaction with garment-based wearable computing devices, (2016). https://doi.org/10.18419/opus-8953.
    11. Seyboth, G.: On Distributed and Cooperative Control Design for Networks of Dynamical Systems, (2016).
    12. Sonntag, M.: Model-as-you-go - ein Ansatz zur flexiblen Entwicklung von wissenschaftlichen Workflows, (2016). https://doi.org/10.18419/opus-8798.
    13. Wengert, N.: Gekoppelte dynamisch-optische Simulation von Hochleistungsobjektiven, (2016).
    14. Zeng, S.: Ensemble observability of dynamical systems, (2016).

  10. 2015

    1. Alebrand, S.: Efficient schemes for parameterized multiscale problems, (2015). https://doi.org/10.18419/opus-5150.
    2. Benzing, A.: Distributed stream processing in a global sensor grid for scientific simulations, (2015). https://doi.org/10.18419/opus-3603.
    3. Bohner, M.U.: Using umbrella integration to find minimum free energy paths, (2015). https://doi.org/10.18419/opus-1440.
    4. Braun, C.: Algorithm-based fault tolerance for matrix operations on graphics processing units : analysis and extension to autonomous operation, (2015). https://doi.org/10.18419/opus-3545.
    5. Dihlmann, M.: Adaptive reduced basis methods for parameterized evolution problems with application in optimization and state estimation, (2015).
    6. Dürr, H.-B.: Constrained extremum seeking : a lie bracket and singular perturbation approach, (2015).
    7. Heidlauf, T.: Chemo-electro-mechanical modelling of the neuromuscular system, (2015). https://doi.org/10.18419/opus-658.
    8. Hlawatsch, M.: Visualization for integrated simulation systems, (2015). https://doi.org/10.18419/opus-8734.
    9. Kramer, A.: Stochastic methods for parameter estimation and design of experiments in systems biology, (2015).
    10. Röhm, D.: Multiscale simulations of soft and hard matter, (2015). https://doi.org/10.18419/opus-5175.
    11. Schittler, D.: A mathematical modeling framework to simulate and analyze cell type transitions, (2015).
    12. Sommer, T.: Entwicklung und Bewertung von Lagerstrategien zur Steigerung der Energie-effizienz in automatischen Hochregallagern unter Beachtung des Umschlags, (2015). https://doi.org/10.18419/opus-4615.
    13. Sprenger, M.: A 3D continuum-mechanical model for forward-dynamics simulations of the upper limb, (2015). https://doi.org/10.18419/opus-8777.
    14. Ullrich, M.: Monotonicity-based methods for inverse parameter identification problems in partial differential equations, (2015). https://doi.org/10.18419/opus-5171.
    15. Veenman, J.: A general framework for robust analysis and control : an integral quadratic constraint based approach, (2015).
    16. Weber, P.: Data-driven modeling of molecular interactions at the trans-Golgi network of mammalian cells, (2015).
    17. Weise, M.: Einflüsse der mikroskaligen Oberflächengeometrie von Asphaltdeckschichten auf das Tribosystem Reifen-Fahrbahn, (2015). https://doi.org/10.18419/opus-640.
    18. Zeiler, C.: Liquid vapor phase transitions : modeling, Riemann solvers and computation, (2015).
    19. Zinatbakhsh, S.: Coupled problems in the mechanics of multi-physics and multi-phase materials, (2015). https://doi.org/10.18419/opus-632.

  11. 2014

    1. Baber, K.: Coupling free flow and flow in porous media in biological and technical applications : from a simple to a complex interface description, (2014). https://doi.org/10.18419/opus-594.
    2. Benson, S.P.: Molecular modeling of hydrophobic effects in complex biomolecular systems : from simple mixtures to protein-interface aggregation, (2014). https://doi.org/10.18419/opus-1442.
    3. Frey, S.: Strategies for efficient parallel visualization, (2014). https://doi.org/10.18419/opus-6460.
    4. Geiges, A.: Efficient concepts for optimal experimental design in nonlinear environmental systems, (2014). https://doi.org/10.18419/opus-612.
    5. Kratzer, K.: Crystallization pathways and mechanisms of charged macromolecules at low supersaturations, (2014). https://doi.org/10.18419/opus-5142.
    6. Krause, R.F.: Growth, modelling and remodelling of biological tissue, (2014). https://doi.org/10.18419/opus-616.
    7. Schmidt, G.S.: Synchronization of oscillators and global output regulation for rigid body systems, (2014).
    8. Sorg, A.: Adaptive diskret-kontinuierliche Modellierung von Materialien mit Mikrostruktur, (2014). https://doi.org/10.18419/opus-600.
    9. Weeber, R.: Simulation of novel magnetic materials in the field of soft matter, (2014). https://doi.org/10.18419/opus-5144.

Dissertations not in PUMA

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