OPUS documents https://opus4.kobv.de/opus4-zib/index/index/ Tue, 21 Jul 2026 12:01:29 +0200 Tue, 21 Jul 2026 12:01:29 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10406 This chapter describes the process of digitally unfolding rolled and folded written documents of different materials such as parchment, papyrus, paper, silver, or lead. The folded state of the documents often conceals the information contained in them from the observer, making it difficult to access. Digital unfolding is necessary to prevent damage to the precious historical artifacts, which might occur if one tried to unfold the documents physically to access hidden information. The first step in this digital process is the acquisition of three-dimensional (3D) image data from the document itself, followed by the segmentation of the writing material in the 3D image. Afterward, the segmented material is digitally flattened to obtain an unobscured view of the script. Finally, visualization of the flattened document allows one to make the writing visible. Each of these steps presents distinct difficulties. This chapter outlines the general workflow, together with some key strategies available to overcome these obstacles. Finally, it discusses some remaining core issues and challenges. Nicolas Klenert; Daniel Baum bookpart https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10406 Tue, 21 Jul 2026 12:01:29 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10405 Chukwuebuka William Okafornta; Reza Farhadifar; Gunar Fabig; Hai-Yin Wu; Maria Köckert; Martin Vogel; Daniel Baum; Robert Haase; Michael J. Shelley; Daniel J. Needleman; Thomas Müller-Reichert article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10405 Tue, 21 Jul 2026 08:26:35 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10404 Anterior knee pain (AKP) remains as one of the most cited causes of patient’s dissatisfaction after total knee arthroplasty (TKA) with non-physiological knee joint kinematics patterns during flexion and patellar maltracking being frequently associated with it. Incorporating in vivo knee joint kinematics into the design of new TKA systems could significantly reduce these negative outcomes. This study aims to show the outcome of a new TKA system developed considering previously assessed in vivo knee joint kinematics. Retrospective study design. Eleven patients treated with the 5C® LATic TKA system were included. In-vivo fluoroscopic measurements in loaded lunge to collect the 3D TKA motion from extension to maximal flexion was conducted at 12 months after index surgery. All patients answered the KSS, FJS and HFKS questionnaires. The kinematics of the 5C® LATic system were characterized by a lateral stability as well as reduced anterior movement in the medial compartment, resulting in an external rotation of the femoral component of the tibia plateau. Moreover, the frontal points reveal a consistent posterior movement of the frontal aspect until maximal achieved flexion. All patients showed improvements in all administered questionnaires compared to their preoperative state, with high overall satisfaction reported for the prosthesis. The results show an expected design-based kinematics. Although the posterior translation of the frontal aspect of the femoral component can only be interpreted as an indirect measure of patellar movement, the increased motion observed, together with the external rotation on the tibia plateau, may contribute to improvement in patellar tracking, which is recognized as a direct contributor to AKP Philippe Moewis; Rainald Ehrig; Leonie Krahl; Adam Trepczynski; Jonas Tykfer; Hagen Hommel; Georg N. Duda article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10404 Wed, 15 Jul 2026 14:26:01 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10403 Julian N. Zierke; Philippe Moewis; Rainald Ehrig; William R. Taylor; Adam Trepczynski; Georg N. Duda; Philipp Damm article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10403 Wed, 15 Jul 2026 14:25:53 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10400 In railway traffic, delays occur every day, causing deviations from the planned timetable and potentially leading to significant delays for passengers, crew members, and trains. In such cases, the original timetable may become infeasible. Therefore, the delay management problem focuses on the construction of a disposition timetable that minimizes the inconvenience for passengers. In this context, two dispatching decisions must be made: wait–depart decisions, which determine whether trains should wait for delayed feeder trains in order to maintain passenger transfers, and precedence decisions, which define the order of trains while respecting the limited capacity of the railway network. For the extensively researched Offline Delay Management Problem (ODM), it is assumed that source delays are fully known and stable over an extended planning period. In this thesis, we relax this assumption and propose the Stochastic Delay Management Problem (DM), which handles source delays as constant within an initial control horizon and as random variables following a delay distribution thereafter. We introduce a two-stage stochastic mixed-integer linear programming formulation for the problem and prove NP-hardness for a special case. Moreover, we analyze different fixations of the wait-depart and precedence decisions. For those, we derive bounds for optimal solutions as well as for the optimal objective values, including tightness results. In practice, numerous simple dispatching rules are applied to guide decisions. To achieve comparability with DM, we integrate seven different rules for wait-depart and precedence decisions into our optimization framework. We then study the price of the rules and show that each examined rule can lead to arbitrarily poor disposition timetables compared to DM. In particular, we present an algorithm corresponding to the case where no dispatching is performed and study the price of non-dispatching. We compare the performance of DM to the performance of the Offline Delay Management Problem and the seven dispatching rules in a computational experiment for the Berlin S-Bahn with different delay scenarios. It becomes evident that taking stochastic delays into account yields on average 1.7% better disposition timetables, but at the cost of higher runtime. Additionally, the average price of non-dispatching of 2.7% indicates that dispatching is desirable, but not as much as one might expect. Three dispatching rules achieve similarly satisfactory results compared to DM. In particular, the rule that neglects all wait-depart decisions finds similarly good or slightly better disposition timetables than DM within a marginally shorter average runtime. Patricia Ebert masterthesis https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10400 Tue, 14 Jul 2026 15:23:42 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10399 A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess rich geometric form, giving rise to shape spaces that encode variability in object geometry. Such datasets arise across a wide range of disciplines, including biology, medicine, anthropology, and computer vision, where subtle geometric differences often carry important scientific information. Traditional machine learning methods, however, are frequently ill-equipped to account for the nonlinear geometric structure underlying these data. This survey synthesizes a rapidly growing body of work on shape space analysis, which provides a mathematical and computational framework for the study of geometric data. Drawing on ideas from differential geometry, statistics, and machine learning, we organize the literature around a common analytical pipeline: shape representation and parameterization, the rigorous construction of robust geodesic metrics, statistical analysis on shape spaces, and geometry-aware learning methods. We discuss how these tools enable the characterization of shape variability, the comparison of geometric objects, and the analysis of structural trajectories across populations and time. To illustrate the breadth of the field, we highlight applications spanning multiple scales of biological organization, including studies of subcellular morphology and primate tooth evolution. Across these and many other domains, researchers face common challenges arising from complex, nonlinear, and often unaligned geometric variation. The review concludes by identifying key theoretical and computational challenges, as well as emerging opportunities driven by increasingly large and diverse geometric datasets. Gary P. T. Choi; Khanh Dao Duc; Shira Faigenbaum-Golovin; Karen Habermann; Emmanuel Hartman; Christoph von Tycowicz; Chi Zhang; Wenjun Zhao; Felix Zhou article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10399 Fri, 10 Jul 2026 16:14:08 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10398 Robert Clausecker; Florian Schintke article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10398 Thu, 09 Jul 2026 14:38:18 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10397 Robert Clausecker; Daniel Lemire article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10397 Thu, 09 Jul 2026 14:33:00 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10396 Robert Clausecker; Florian Kurpicz; Etienne Palanga article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10396 Thu, 09 Jul 2026 14:26:19 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10394 Robert Clausecker; Florian Kurpicz; Etienne Palanga conferenceobject https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10394 Thu, 09 Jul 2026 14:09:20 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10392 Ayhan Demircan; Andrey B. Evlyukhin; Uwe Morgner; Oliver Melchert; Ihar Babushkin; Shihao Zhang; Fabio Biancalana; Chao Mei; Jintao Fan; Peilong Yang; Günter Steinmeyer; Philippe Lalanne; Owen D Miller; Hooman Barati Sedeh; Natalia M Litchinitser; Virgilijus Vaičaitis; Ona Balachninaitė; Osamu Takayama; Radu Malureanu; Andrei V Lavrinenko; Benjamin Wetzel; Bruno P Chaves; Alessandro Tonello; Vincent Couderc; Vladimir R Tuz; Anton Husakou; Luc Bergé; Xavier Davoine; Laurent Gremillet; Jan Vábe; Fabrice Catoire; Stefan Skupin; Pavel Polynkin; Jacob Barker; John Palastro; Sergey Sergeyev; Hani Kbashi; Sergei K Turitsyn; Evgenii Menshikov; Paolo Franceschini; Costantino De Angelis; Yuri S Kivshar; [...]; Felix Binkowski; Fridtjof Betz; Martin Hammerschmidt; Philipp-Immanuel Schneider; Lin Zschiedrich; Sven Burger; [...]; Olivier J. F. Martin; Pascal Krause; Annika Bande article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10392 Wed, 08 Jul 2026 14:48:46 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10389 Superatoms are clusters with physical or chemical properties that bear certain similarities to atoms. We calculated equilibrium geometries and properties using spin-polarized density functional theory (DFT) within the generalized gradient approximation (PBE). We also performed Ab Initio Molecular Dynamics (AIMD) simulations in the NVT ensemble with a Nosé-Hoover thermostat. In addition, Monte Carlo simulations with a potential fitted to DFT were carried out to assess a number of clusters as possible superatoms. The clusters we studied have a chemical composition AB2C10, where A, B, and C are metals, and a structure derived from that of a 13-atom icosahedron. We adopt an operational definition of superatom to assess clusters. According to this definition, superatoms should have (i) a quasispherical equilibrium geometry and (ii) a set of valence orbitals delocalized over the cluster; (iii) they should maintain these two attributes at elevated temperatures (mechanical and thermal stability); and (iv) they should be thermodynamically favored relative to similar clusters. By these criteria, Sn3Y10, CrSn2Zr10, and possibly CrSn2Ti10 can be classified as “superatoms”. We show that cluster stability arises from (i) the intrinsic stability of the C10 fragment; (ii) its interaction energy with the three “dopant atoms”; and (iii) compatible atomic sizes of the three elements. Clusters such as CrSn2Ti10 and CrSn2Zr10 exhibit an optimal balance among these contributions. They have a high effective coordination, an absence of very low vibrational frequencies, a favorable ratio of their elements’ cohesive energy, and exceptional thermal resilience, with melting temperatures that are well above the weighted average of their elements’ bulk melting points. Electronic analysis shows that CrSn2Ti10 and CrSn2Zr10 display discrete density-of-states features and partial electron delocalization, consistent with superatomic behavior. These electronic properties vanish as the clusters approach the melting point and undergo structural distortion. We show the importance of looking at several, often interrelated, properties in assessing possible superatoms. Anirudh Krishnadas; João Marcos Tomaz Palheta; Jonathan Bekele Mekonnen; Renato Luis Tame Parreira; Efracio Mamani Flores; René Fournier; Maurício Jeomar Piotrowski article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10389 Wed, 08 Jul 2026 14:04:56 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10385 Lu Qiao; Ronaldo Rodrigues Pelá; Claudia Draxl article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10385 Tue, 07 Jul 2026 12:28:03 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10386 This paper presents a hybrid modelling approach that couples an Agent-Based Model (ABM) with a partial differential equation (PDE) model in an epidemic setting to simulate the spatial spread of infectious diseases using a compartmental structure with seven health states. The goal is to reduce the computational complexity of a full-ABM by introducing a coupled ABM-PDE model that offers significantly faster simulations while maintaining comparable accuracy. Our results demonstrate that the hybrid model not only reduces the overall simulation runtime (defined as the number of runs required for stable results multiplied by the duration of a single run) but also achieves smaller errors across both 25% and 100% population samples. The coupling mechanism ensures consistency at the model interface: agents crossing from the ABM into the PDE domain are removed and represented as density contributions, while surplus density in the PDE domain is used to generate agents with plausible trajectories derived from mobile phone data. We evaluate the hybrid model using real-world mobility and infection data for the Berlin-Brandenburg region in Germany, showing that it captures the core epidemiological dynamics while enabling efficient large-scale simulations. These results demonstrate that the proposed ABM-PDE framework provides a robust and computationally efficient alternative to full-scale agent-based simulations, making it suitable for realistic epidemic modelling and scenario analysis. Kristina Kehrer; Tim O.F. Conrad article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10386 Tue, 07 Jul 2026 11:15:17 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10380 Abstract Background Clinically, fracture healing is typically monitored though serial radiographs. Specifically, callus development (growth and mineralization) is an indicator of healing and associated with local mechanical strain. However, a sustainable relationship between mechanical conditions and the respective healing progress has not been shown so far. Material and methods One hundred sixty-six patients with extra-articular lower-limb fractures treated by osteosynthesis plates or intramedullary nails were included. Callus formation (visible area in X-ray relative to bone shaft) and quality (image intensity relative to the cortex) were measured by consecutive X-ray analyses as well as the modified Radiographic Union-Score for Tibia (mRUST) during follow-up. Corresponding load- and fixation-matched finite element analysis (FEA) modelling was developed for tibia or femur loading as well as plate or intramedullary nail fixation. Mechanical strains (medially, laterally, dorsally, anteriorly) were evaluated from FEA and compared to the progress in X-ray callus formation and quality to perform a correlation analysis between observed callus formation and simulated local mechanical strains. Results For femoral fractures, callus size was largest dorso-medially (1.41 ± 1.57 cm 2 /cm and 1.18 ± 1.11 cm 2 /cm at 180 ± 45 days post-surgery) while largest callus formations were found laterally in tibial fractures (0.75 ± 0.49 cm 2 /cm at 365 ± 45 days post-surgery). These locations of maximal callus size in femur and tibia matched the locations of extreme principal strains from FEA. In femur, callus density increased steadily and exceeded cortex density at 365 ± 45 days post-surgery. For tibia, no such clear trend was observable. While initially showing a similar increase in callus bridging score mRUST, increase over 2 years was 48% higher for the tibial fractures compared to femoral fractures. While principal strains correspond to increases in early callus formation in both femur and tibia (Kendall-Tau-b: p  = 0.021 for volumetric strain at 90 ± 45 days post-surgery), shear strains are consistently associated with less callus formation (Kendall-Tau-b: p  = 0.048 for volumetric/shear strain associated with callus size*density at 365 ± 45 days post-surgery). Conclusions Callus formation during bone healing may be associated with local mechanical strain in lower limb fractures within a clinically relevant cohort including different fracture locations and fixation types. Shear strain at the fracture site appeared to be associated with reduced quality callus formation, whereas principal strain was observed to correlate with increased early callus formation. The presented methodology may have potential as a predictor of healing processes and could help identify mechanically challenging fracture fixation settings. Level of evidence II. Trial registration Ethical approval was obtained from the local ethics board to this retrospective study design (EA4/099/24). Mark Heyland; Dominik Deppe; Matteo Gabriele; Julian N. Zierke; Marko Leskovar; Marie-Jacqueline Reisener; Yuriy Rozhko; Katharina Ziegeler; Philipp Damm; Simon Reinke; Ulrich Stöckle; Georg N. Duda; Stefan Zachow; Adam Trepczynski article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10380 Mon, 06 Jul 2026 12:11:12 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10378 3D shape reconstruction is an active area of research and a fundamental problem in computer vision, with growing applications in the medical domain, where it enables the recovery of missing or fine anatomical structures. Numerous approaches have been proposed for medical shape reconstruction, emphasizing accurate and anatomically plausible reconstructions. However, 3D shape reconstruction remains an inherently ill-posed problem, meaning (1) both neural network-based methods and conventional shape modeling approaches naturally introduce uncertainty in their predictions, and (2) multiple anatomically plausible reconstructions exist for a given partial or low-resolution input. While the uncertainty aspects have been widely explored in general computer vision, they remain relatively under-explored in the context of medical shape reconstruction. In this paper, we developed a 3D Bayesian U-Net and investigated its use for uncertainty estimation and probabilistic reconstructions across three key tasks: cranial reconstruction, facial reconstruction, and skull shape super-resolution. Our findings show that the Bayesian model is able to produce a range of anatomically plausible skull reconstructions while capturing natural skull variations arising from the learned weight uncertainty. Notably, these variations are primarily expressed through differences in bone thickness, which aligns with anatomical expectations, particularly relevant in real-world applications like cranial implant design. Additionally, we propose a principled framework to study the relationship between weight uncertainty and reconstruction uncertainty by analyzing the learned posterior distribution of the weights, demonstrating that our Bayesian U-Net achieves comparable reconstruction performance to a deterministic U-Net baseline while providing reliable uncertainty estimates. Our study also reveals a clear cross-task uncertainty pattern, where tasks with stronger structural constraints, like super-resolution, yield lower predictive uncertainty, while less constrained tasks, like facial reconstruction, result in higher uncertainty. Refer to the project page for more visual results https://git.zib.de/jli/uncertainty-aware-skull-reconstruction/. Li Jianning; Agniva Sengupta; Stefan Zachow article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10378 Mon, 06 Jul 2026 11:15:43 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10376 Temporal (or time-evolving) networks provide a natural framework for modeling complex systems with time-dependent interactions, where understanding the evolution of community structures is a central challenge. While random walk-based approaches to community detection in static networks are well established through the spectral analysis of associated transfer operators, extending these ideas to temporal networks is nontrivial due to the inherent time-dependence of the underlying dynamics. In this work, we develop a general framework for community detection in temporal networks that is based on multi-view canonical correlation analysis (mCCA). We show that the proposed formulation admits a spectral characterization via a time-reversible random walk on an augmented space-time network, providing a clear dynamical interpretation of temporal communities as metastable structures of the process. Furthermore, we analyze key spectral properties of the resulting transfer operators and the interplay between spatial and temporal effects, which allows us to distinguish between structural features and artifacts induced by the snapshot coupling. Finally, we derive a reduced-order model, which preserves the essential spectral properties while significantly improving computational efficiency. We show that the proposed approach effectively detects communities in temporal networks and captures their evolution. Filip Blaskovic; Stefan Klus; Tim Conrad; Natasa Djurdjevac Conrad article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10376 Wed, 01 Jul 2026 13:32:25 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10375 Thorsten Koch; David E. Bernal Neira; Ying Chen; Giorgio Cortiana; Daniel J. Egger; Raoul Heese; Narendra N. Hegade; Alejandro Gomez Cadavid; Rhea Huang; Toshinari Itoko; Thomas Kleinert; Pedro Maciel Xavier; Naeimeh Mohseni; Jhon A. Montanez-Barrera; Koji Nakano; Giacomo Nannicini; Corey O’Meara; Justin Pauckert; Manuel Proissl; Anurag Ramesh; Maximilian Schicker; Noriaki Shimada; Mitsuharu Takeori; Víctor Valls; David Van Bulck; Stefan Woerner; Christa Zoufal article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10375 Fri, 26 Jun 2026 09:57:41 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10374 Integrating neural networks into mechanistic, equation-based models is a field of growing scientific interest, yet it lacks consistent terminology and a unified mathematical framework. We systematise existing approaches and establish new connections between hybrid modelling, differential equations theory, and deep learning. We clarify the roles of inference, prediction, and generalisation in hybrid models, showing how neural components can capture mathematical structures within and across datasets, and we connect these questions to structural identifiability theory. We further interpret hybrid models through a manifold-learning lens, demonstrating how parametric spaces of missing information can be learned, thereby providing an alternative approach to uncertainty quantification. Thomas Gaskin; Anastasia Bankowski; Stefanie Winkelmann article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10374 Thu, 25 Jun 2026 09:38:10 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10373 We investigate transient clustering dynamics in nonlocal aggregation-diffusion systems from an energetic perspective. Starting from a stochastic interacting particle system, we study the associated macroscopic McKean-Vlasov equation on the torus and exploit its Wasserstein gradient-flow structure to analyse the thermodynamic competition between interaction-driven aggregation and entropy-driven diffusion. Through numerical experiments for locally attractive interaction kernels, we identify alternating aggregation- and diffusion-dominated transient regimes along trajectories converging to fixed equilibria. These dynamics can be interpreted as a form of non-monotone clustering behaviour. Moreover, we demonstrate that clustering observables, such as the density peak height, are only partially coupled to the underlying energetic mechanisms and therefore do not uniquely characterise the relevant macroscopic transport dynamics. Our results highlight the role of the variational structure not only for equilibrium analysis, but also as a framework for understanding transient clustering phenomena in interacting particle systems. Nathalie Wehlitz; Richard Scherzer; Carsten Hartmann; Stefanie Winkelmann article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10373 Thu, 25 Jun 2026 09:30:02 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10372 Opinions play a crucial role in shaping collective phenomena such as political polarization, cultural integration and demographic change. By continuously changing social environments in which opinions evolve, human migration serves as an important driver of collective opinion formation. While migration and opinion dynamics have both been extensively studied, the few existing models that couple the two are primarily deterministic and therefore cannot capture demographic fluctuations, finite-size effects or stochastic transitions between emergent collective states. To address this limitation, we introduce a unifying stochastic framework for opinion dynamics over migration networks that couples local opinion transitions, demographic processes and migration between communities. The dynamics are formulated through a spatio--temporal master equation, which provides a probabilistic description of the underlying population process. From this microscopic representation, we derive deterministic mean-field equations governing the co-evolution of community sizes and opinion compositions, thereby linking agent-level interactions to macroscopic population behavior. Using two representative case studies, we demonstrate how stochasticity and migration can qualitatively change the emergent dynamics and collective outcomes, including the emergence of consensus, polarization and the stabilization of oscillatory opinion dynamics. These examples highlight the rich interplay between social interactions, demographic change and migration in deterministic and stochastic settings, and they demonstrate that migration should be viewed as an integral component of collective opinion formation rather than only an external demographic process. Lorinc Márton; Stefanie Winkelmann; Mauricio del Razo; Natasa Djurdjevac Conrad article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10372 Thu, 25 Jun 2026 09:07:24 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10371 Stochastic network rewiring processes, in which edges dynamically rewire based on fixed node attributes, are widely used in applications ranging from social dynamics to neuroscience and form an important component of adaptive network modelling. In this paper, we identify low-dimensional collective variables (CVs) that capture the essential macroscopic behavior of such time-evolving networks and enable reduced-order descriptions of their dynamics. To this end, we apply the data-driven transition manifold approach to homophily-driven rewiring models, in which edges preferentially connect nodes with similar attributes. For two representative models, we find that the optimal CV is a consensus measure quantifying the fraction of edges whose incident nodes differ by less than a certain threshold. Building on the learned CV, we construct reduced macroscopic models using a data-driven approach based on sparse regression and through an analytical derivation using graphons. The latter yields a closed-form evolution equation for the consensus measure and analytically validates the identified CV. Sören Nagel; Stefanie Winkelmann; Peter Koltai; Natasa Djurdjevac Conrad; Marvin Lücke article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10371 Thu, 25 Jun 2026 08:53:24 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10363 Alonso Martínez Cisneros masterthesis https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10363 Fri, 12 Jun 2026 15:01:09 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10362 Particle-based models are crucial for capturing stochastic dynamics in a diverse range of fields, including biochemistry, social dynamics, and optimization. These systems often exhibit behavior across multiple spatial or temporal scales, requiring methods that can adapt to multiscales. However, particle-based models lack a rigorous theoretical foundation due to their complexity. In biochemistry, a key challenge in particle-based modeling is developing simulations that operate consistently across the microscopic and macroscopic scale while remaining computationally feasible. This thesis establishes a mathematically consistent coupling between particle-based simulations and reactiondiffusion partial differential equations (PDEs). To this end, we derive the Kurtz representation of the reaction-diffusion master equation (RDME) and we extend the chemical diffusion master equation (CDME) to an open setting. Then, we connect thermodynamic properties across scales and incorporate cross-scale interactions by matching the mean-fields of the particle-based models (RDME and CDME) with the PDE-based dynamics. Two new hybrid numerical schemes are developed to efficiently couple particle-based domains with a concentration reservoir. Like in simulation, a key challenge in particle-based optimization is adapting effectively to multiscale landscapes. This thesis develops a novel parameter selection framework for the Particle Swarm Optimization (PSO) algorithm to navigate a multiscale loss landscape. We define new parameter regions based on the moments of particles, which characterize the exploration and exploitation of the optimization algorithm, along with suitable measures for exploration and exploitation. Building on the parameter selection framework, we show that our PSO framework identifies a greater variety of solutions when inferring cultural diffusion of Roman influence. Margarita Kostré doctoralthesis https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10362 Fri, 12 Jun 2026 13:49:25 +0200 https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10361 Gemma Giliberti; Guillermo Farias-Basulto; Klaus Jäger; Thede Mehlhop; Christian A. Kaufmann; Aldo Di Carlo article https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10361 Thu, 11 Jun 2026 16:25:26 +0200