TY - THES A1 - Pakhomov, Anton T1 - Efficient modeling and optimization of surface second-harmonic generation from nanophotonic components Y1 - 2021 U6 - https://doi.org/10.22032/dbt.49369 ER - TY - THES A1 - Moldenhauer, Marian T1 - Adaptive Algorithms in Optimization under PDE Constraints N2 - In this thesis, adaptive algorithms in optimization under PDE constraints have been inves- tigated. In its application, the aim of optimization is to increase the longevity of implants, namely the hip joint implant, and in doing so to minimize stress shielding and simultaneously minimize the influence of locally high stresses, that, above a threshold value, are malign to the bone structure. Under the constraint of the equilibrium of forces, describing an elastodynamic setup, coupled with a contact inequality condition, a computationally expensive problem formulation is given. The first step to make the solution of the given problem possible and efficient was to change over to the spatial equilibrium equation, thus rendering an elastostatic setup. Subsequently the intrinsically dynamic motions – trajectories in the load domain – were converted to the static setup. Thus, the trajectories are marginalized to the load domain and characterized with probability distributions. Therefore the solving of the PDE constraint, the contact problem, is simplified. Yet in the whole optimization process, the solving of the PDE, the spatial equilibrium equation together with the contact condition has the most expensive contribution still and hence needed further reduction. This was achieved by application of Kriging interpolation to the load responses of the integrated distribution of stress difference and the maximum stresses. The interpolation of the two response surfaces only needs comparatively few PDE solves to set up the models. Moreover, the Kriging models can be adaptively extended by sequentially adding sample-response pairs. For this the Kriging inherent variance is used to estimate ideal new sample locations with maximum variance values. In doing so, the overall interpolation variance and therefore the interpolation error is reduced. For the integration of the integrated stress differences and penalty values on the relative high dimensional load domain Monte Carlo integration was implemented, averting the curse of dimension. Here, the motion’s probability distribution combined with patient specific data of motion frequencies is taken advantage of, making obsolete the use of the otherwise necessary importance sampling. Throughout the optimization, the FE-discretization error and the subsequently attached errors entering the solution process via PDE discretization and approximative solving of the PDE, Kriging interpolation and Monte Carlo integration need to decrease. While the FE-discretization error and the solution of the elastostatic contact problem were assumed precise enough, numerics showed, that the interpolation and integration errors can be controlled by adaptive refinement of the respective methods. For this purpose comparable error quantities for the particular algorithms were introduced and effectively put to use. For the implant position’s optimization, the derivative of the objective function was derived using the implicit function theorem. As the FE-discretization changes with implant position modifications big enough, a special line search had to be used to deal with the discontinuities in the objective function. The interplay and performance of the subalgorithms was demonstrated numerically on a reduced 2D setup of a hip joint with and without the implant. Consequently the load domain and the control variable were also limited to the 2D case. Y1 - 2021 ER - TY - THES A1 - Zonker, Johannes T1 - Coarse Graining of Agent-Based Models and Spatio-Temporal Modeling of Spreading Processes Y1 - 2023 UR - http://dx.doi.org/10.17169/refubium-41220 ER - TY - THES A1 - Binkowski, Felix T1 - Riesz-projection-based methods for the numerical simulation of resonance phenomena in nanophotonics Y1 - 2023 U6 - https://doi.org/10.17169/refubium-41230 ER - TY - THES A1 - Plock, Matthias T1 - On Methods for Bayesian Optimization of Least Squares Problems and Optimization of Nanophotonic Devices Y1 - 2025 U6 - https://doi.org/10.17169/refubium-48179 ER - TY - THES A1 - Lücke, Marvin T1 - Concentration effects and collective variables in dynamical systems on networks Y1 - 2024 U6 - https://doi.org/10.17169/refubium-45325 ER - TY - THES A1 - Ernst, Ariane T1 - Mathematical modeling and analysis of neurotransmission N2 - Modeling and simulation of neurotransmission plays a central role in the scientific investigation of synaptic functioning and neuronal communication. Understanding the neurotransmission process is crucial due to its fundamental significance in cognitive function, with impairments in this process potentially giving rise to severe pathological conditions. Existing modeling approaches capture the dynamic behavior and the probabilistic properties of the synaptic machinery utilizing results from stochastic chemical kinetics. However, the underlying mathematical theory rarely receives detailed consideration. In this thesis, we explore the neurotransmission process from a mathematical perspective and provide important insights that improve efficiency in its modeling and simulation. We introduce a new method for the direct and exact computation of first- and second-order moments of the postsynaptic current for the case of linear reaction networks, circumventing previously employed stochastic simulations. The approach hinges on the computation of the cross-correlation functions for the associated Markov jump process and their characterization by a partial differential equation. Our method can be applied to efficiently compare coarse-graining strategies for neurotransmission models, which we demonstrate for a significant recent model of synaptic vesicle release. We also numerically analyze the feasibility of reducing this particular model and show that a reduction can preserve the characteristics of the mean output current and its standard deviation to a satisfactory degree under typical stimulation conditions, where the approximation error can even be neglected depending on release site position. As the dependency on the local calcium concentration is preserved in the reduced model, it can be subjected to arbitrary types of signals in future studies. Moreover, we investigate the recovery processes of synaptic vesicles and release sites by introducing a nonlinear model with explicit recovery reactions. A sensitivity analysis provides the novel insight that the widely debated identity of the limiting recovery process during sustained stimulation is time-dependent. For our model, the output current’s first-order moment and its deterministic approximation show an exceptionally high level of agreement. We determine this to be an inherent consequence of the model structure due to the independence of the two recovery processes, supporting future use of the deterministic approximation. Parameter estimations confirm our model’s capability of reproducing experimental data. Y1 - 2024 ER - TY - THES A1 - Chegini, Fatemeh T1 - Multilevel optimization algorithm for Inverse Problem in Electrocardiography N2 - The electric conductivity of cardiac tissue determines excitation propagation and is vital for quantifying ischemia and scar tissue and building personalized models. As scar tissue is generally characterized by different conduction of electrical excitation, we aim to estimate conductivity-related parameters in mathematical excitation models from endocardial mapping data, particularly the anisotropic conductivity tensor in the monodomain equation, which describes the cardiac excitation. Yet, estimating the distributed and anisotropic conductivity tensors reliably and efficiently from endocardial mapping data or electrocardiograms is a challenging inverse problem due to the computational complexity of the monodomain equation; Many expensive high-resolution computations for the monodomain equation on very fine space and time discretizations are involved. Thus, we aim at building an efficient multilevel method for accelerating the estimation procedure combining electrophysiology models of different complex- ity, which uses a computationally cheap eikonal model in addition to the more accurate monodomain model. Distributed parameter estimation, well-known as an ill-posed inverse problem, can be performed by minimizing the misfit between simulated and measured electrical activity on the endocardial surface subject to the monodomain model and some regularization, leading to a partial differential equation constrained optimization problem. We formulate this optimization problem, including scar tissue modeling and different regularizations, and design an efficient iterative solver. To this aim, we consider monodomain grid hi- erarchies, monodomain-eikonal model hierarchies, and the combination of both hierarchies in a recursive multilevel trust-region (RMTR) method. On the one hand, both the trust region method’s estimation quality and efficiency, independent of the data, are investigated from several numerical exam- ples. Endocardial mapping data of realistic density appears to be sufficient to provide quantitatively reasonable estimates of the location, size, and shape of scars close to the endocardial surface. In several situations, scar reconstruction based on eikonal and monodomain models differ significantly, suggesting the use of the more involved monodomain model for this purpose. Moreover, Eikonal models can accelerate the computations considerably, enabling the use of complex electrophysiology models for estimating myocardial scars from endocardial mapping data. In many situations, eikonal models approximate monodomain models well but are orders of magnitude faster to solve. Thus, eikonal models can utilize them to provide an RMTR acceleration with negligible overhead per iteration, resulting in a practical approach to estimating myocardial scars from endocardial mapping data. In addition, the multilevel solver is faster than a comparable single-level solver. On the other hand, we investigate different optimization approaches based on adjoint gradient computation for computing a maximum posterior estimate: steepest descent, limited memory BFGS, and recursive multilevel trust region methods using mesh hierarchies or heterogeneous model hierarchies. We compare overall performance, asymptotic convergence rate, and pre-asymptotic progress on selected examples in order to assess the benefit of our multifidelity acceleration. Y1 - 2022 ER - TY - THES A1 - Tillmann, Peter T1 - Optimizing bifacial tandem solar cells for realistic operation conditions Y1 - 2023 U6 - https://doi.org/10.17169/refubium-39571 ER - TY - THES A1 - Venkatareddy, Narendra Lagumaddepalli T1 - Revealing secrets of mussel-glue mimetic peptides - From advanced NMR to computational process modelling Y1 - 2020 ER -