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arXiv 2025年

作者： Daneshyar, Alireza Kollmannsberger, Stefan Chair of Computational Modeling and Simulation Technical University of Munich Germany Data Science in Civil Engineering Bauhaus-Universität Weimar Germany

We begin by addressing the time-domain full-waveform inversion using the adjoint method. Next, we derive the scaled boundary semi-weak form of the scalar wave equation in heterogeneous media through the Galerkin method. Unlike conventional scaled boundary finite element formulations, the resulting system incorporates variable density and two additional terms involving its spatial derivative. As a result, the coefficient matrices are no longer constant over the elements but instead depend on the so-called radial coordinate, rendering the common solution methods such as low-frequency expansion and continued fractions—both of which assume that these matrices can be defined solely by boundary values—inapplicable. In this context, we introduce a radial discretization scheme for solving the transient scalar wave equation with spatially varying density within the framework of the scaled boundary finite element method. This approach begins with temporal discretization in the time domain, followed by spatial discretization along the radial coordinate. We employ the Newmark method for the former and a finite differencing scheme for the latter. However, the choices underlying our ansatz are made for demonstration purposes and remain flexible. Following these discretizations, we introduce an algorithmic condensation procedure to compute the dynamic stiffness matrices of the elements on the fly. Therefore, we maneuver around the need to introduce auxiliary unknowns into the global algebraic system. As a result, the optimization problem is structured in a two-level hierarchy. The higher level computes the nodal wavefields on a relatively coarse mesh, while the lower level provides the gradient of the cost function for a finely distributed set of model parameters. We obtain the Fréchet kernel by computing the zero-lag cross-correlations of the forward and adjoint wavefields, and solve the minimization problems iteratively by moving downhill on the cost function hypersurface throug

关键词： Cost functions

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A plastic damage model with mixed isotropic-kinematic hardening for low-cycle fatigue in 7020 aluminum

arXiv

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arXiv 2025年

作者： Daneshyar, Alireza Siebert, Dorina Radlbeck, Christina Kollmannsberger, Stefan Chair of Computational Modeling and Simulation Technical University of Munich Germany Chair of Metal Structures Technical University of Munich Germany Data Science in Civil Engineering Bauhaus-Universität Weimar Germany

The paper at hand presents an in-depth investigation into the fatigue behavior of the high-strength aluminum alloy EN AW-7020 T6 using both experimental and numerical approaches. Two types of specimens are investigated: a dog-bone specimen subjected to cyclic loading in a symmetric strain-controlled regime, and a compact tension specimen subjected to repeated loading and unloading, which leads to damage growth from the notch tip. Experimental data from these tests are used to identify the different phases of fatigue and to establish the requirements for an appropriate constitutive model. Subsequently, a plastic-damage model is developed, incorporating J2 plasticity with Chaboche-type mixed isotropic-kinematic hardening. A detailed investigation reveals that the Chaboche model must be blended with a suitable isotropic hardening and combined with a proper damage growth model to accurately describe cyclic fatigue including large plastic strains up to failure. Multiple back-stress components with independent properties are superimposed, and exponential isotropic hardening with saturation effects is introduced to improve alignment with experimental results. For damage, different stress splits are tested, with the deviatoric/volumetric split proving successful in reproducing the desired degradation in peak stress and stiffness. A nonlinear activation function is introduced to ensure smooth transitions between tension and compression. Two damage indices, one for the deviatoric part and one for the volumetric part, are defined, each of which is governed by a distinct trilinear damage growth function. The governing differential equation of the problem is regularized by higher-order gradient terms to address the ill-posedness induced by softening. Finally, the plasticity model is calibrated using finite element simulations of the dog-bone test and subsequently applied to the cyclic loading of the compact tension specimen. © 2025, CC BY-NC-ND.

关键词： Cyclic loads

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A CFL condition for the finite cell method

arXiv

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arXiv 2025年

作者： Bürchner, Tim Radtke, Lars Kopp, Philipp Computational Modeling and Simulation Technische Universität München Germany Institute for Ship Structural Design and Analysis Technische Universität Hamburg Germany Chair of Data Science in Civil Engineering Bauhaus-Universität Weimar Germany

Immersed boundary finite element methods allow the user to bypass the potentially troublesome task of boundary-conforming mesh generation. However, they suffer from the influence of cut elements, i.e., elements that are intersected by the physical domain boundaries. When combined with explicit time integration, poorly cut elements with little support in the physical domain have a detrimental effect on the critical time step size, thereby hampering the application of immersed boundary methods to wave propagation simulations. In this paper, we investigate the stabilizing effect of the finite cell method concerning explicit time integration. Starting with an analytical solution of an example with one degree of freedom, we systematically study the influence of α-stabilization on the maximum eigenvalue and thus on the critical time step size. The analysis is then complemented by a numerical study of an example with one element and an increasing polynomial degree. We demonstrate that the critical time step size does not decrease below a certain limit, even when further reducing the cut fraction of the element. This minimum critical time step size is controlled by the chosen α value and becomes less severe for higher dimensions. Increasing the polynomial degree has little effect on the degradation of the minimum critical time step size. Finally, we provide an estimate of the minimum critical time step size depending on the chosen stabilization parameter α and the dimension of the problem. Based on this estimate, we propose a modified CFL condition for the finite cell method, the validity of which we demonstrate on a numerical example of a perforated plate. © 2025, CC BY.

关键词： Boundary element method

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AI-DRIVEN DAY-TO-DAY ROUTE CHOICE

arXiv

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arXiv 2024年

作者： Wang, Leizhen Duan, Peibo He, Zhengbing Lyu, Cheng Chen, Xin Zheng, Nan Yao, Li Ma, Zhenliang Department of Data Science and Artificial Intelligence Monash University Melbourne Australia Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge United States Chair of Transportation Systems Engineering Technical University of Munich Munich Germany School of Civil Engineering The University of Queensland Brisbane Australia Department of Civil Engineering Monash University Melbourne Australia School of Computer Science and Engineering Southeast University Nanjing China Department of Civil and Architectural Engineering KTH Royal Institute of Technology Stockholm Sweden

Understanding travelers’ route choices can help policymakers devise optimal operational and planning strategies for both normal and abnormal circumstances. However, existing choice modeling methods often rely on predefined assumptions and struggle to capture the dynamic and adaptive nature of travel behavior. Recently, Large Language Models (LLMs) have emerged as a promising alternative, demonstrating remarkable ability to replicate human-like behaviors across various fields. Despite this potential, their capacity to accurately simulate human route choice behavior in transportation contexts remains doubtful. To satisfy this curiosity, this paper investigates the potential of LLMs for route choice modeling by introducing an LLM-empowered agent, "LLMTraveler." This agent integrates an LLM as its core, equipped with a memory system that learns from past experiences and makes decisions by balancing retrieved data and personality traits. The study systematically evaluates the LLMTraveler’s ability to replicate human-like decision-making through two stages of day-to-day (DTD) congestion games: (1) analyzing its route-switching behavior in single origin-destination (OD) pair scenarios, where it demonstrates patterns that align with laboratory data but cannot be fully explained by traditional models, and (2) testing its capacity to model adaptive learning behaviors in multi-OD scenarios on the Ortuzar and Willumsen (OW) network, producing results comparable to Multinomial Logit (MNL) and Reinforcement Learning (RL) models. Additionally, the study assesses lightweight, open-source LLMs, highlighting their effectiveness in route choice simulation and their potential as cost-effective alternatives to more advanced closed-source models. These experiments demonstrate that the framework can partially replicate human-like decision-making in route choice while providing natural language explanations for its decisions. This capability offers valuable insights for transportation poli

关键词： Reinforcement learning

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Generating brick-like melt pools for Laser-Based Powder Bed Fusion using flexible beam shaping: a proof of concept on bead-on-plate single tracks

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Procedia CIRP 2024年 124卷 746-750页

作者： Jonas Grünewald Robin Prudlik Vijaya Holla Philipp Kopp Richard Off Thomas Stoll Stefan Kollmannsberger Katrin Wudy Professorship of Laser-based Additive Manufacturing TUM School of Engineering and Design Technical University of Munich Freisinger Landstraße 52 85748 Garching Germany Oerlikon AM Europe GmbH Freisinger Landstraße 52 85748 Garching Germany Chair of Computational Modeling and Simulation TUM School of Engineering and Design Technical University of Munich Arcisstraße 21 80333 Munich Germany Chair of Data Science in Civil Engineering Bauhaus University Weimar Coudraystraße 13A 99423 Weimar Germany

Despite the highest industrial maturity among additive manufacturing processes, laser-based powder bed fusion of metals (PBF-LB/M) lacks the productivity to further establish as an industrial manufacturing process. One approach for adjusting the melt pool shape to increase productivity in PBF-LB/M is spatial beam shaping. The freedom of beam shaping is almost unlimited, owing to modern optical concepts. The key is a beam shape optimized for a specific target. In this publication, the target is a brick-like melt pool cross-section. The publication demonstrates how the corresponding laser beam shape is designed using numerical optimization. The computed beam shape is reproduced using reflective spatial light phase modulators. The calculation is validated based on single-track experiments. Applying the optimized beam shape results in a melt pool shape similar to the optimization target. The width and depth of the melt track produced deviate by a maximum of 7 % from the specified target values.

关键词： Additive Manufacturing PBF-LB/M LCOS-SLM IN718 Beam Shape Optimization

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Fatigue prediction for metallic additively manufactured lattice components using FCM based on average strain energy density approach

Fatigue prediction for metallic additively manufactured latt...

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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： R. De Biasi O. Oztoprak F. Zanini S. Carmignato F. Berto S. Kollmannsberger M. Benedetti Department of Industrial Engineering Via Sommarive 9 University of Trento Trento Italy School of Engineering and Design Chair of Computational Modeling and Simulation Arcisstraße 21 Technical University of Munich Munich Germany Department of Management and Engineering Stradella San Nicola 3 University of Padova Vicenza Italy Department of Chemical Engineering Materials Environment Via Eudossiana 18 University of Rome Sapienza Roma Italy Faculty of Civil and Environmental Sciences Chair of Data Science in Engineering Coudraystraße 13b Bauhaus-Universität Weimar Weimar Germany

ISBN: (数字)9798350373493

ISBN: (纸本)9798350373509

An accurate prediction of the fatigue life of metallic additively manufactured lattice structures is still a challenging task. The proposed work addresses this challenge of industrial relevance through the computation of an as-built geometry of a lattice structure directly from its X-ray computed tomography reconstruction. The computation is carried out by taking advantage of the Finite Cell Method and applies the average strain energy density method to predict fatigue failure. The proof of concept provided in this work shows an excellent agreement in the fatigue prediction, paving the way for a computationally accurate fatigue life prediction of other types of metamaterials.

关键词： Geometry Accuracy Lattices Fatigue Metamaterials Topology X-ray imaging Stress Strain Testing

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Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Network and Systems Medicine

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Network and Systems Medicine 2020年第1期3卷 36-56页

作者： Stalidzans, Egils Zanin, Massimiliano Tieri, Paolo Castiglione, Filippo Polster, Annikka Scheiner, Stefan Pahle, Jürgen Stres, Blaž List, Markus Baumbach, Jan Lautizi, Manuela Van Steen, Kristel Schmidt, Harald H.H.W. Computational Systems Biology Group University of Latvia Riga Latvia Latvian Biomedical Reasearch and Study Centre Riga Latvia Centro de Tecnología Biomédica Universidad Politécnica de Madrid Pozuelo de Alarcón Spain CNR National Research Council IAC Institute for Applied Computing Rome Italy Oslo Norway Institute for Mechanics of Materials and Structures Vienna University of Technology Vienna Austria BioQuant Heidelberg University Heidelberg Germany Department of Animal Science University of Ljubljana Ljubljana Slovenia Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Big Data in BioMedicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Computational Systems Medicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany BIO-Systems Genetics GIGA-R University of Liège Liège Belgium BIO3 - Systems Medicine Department of Human Genetics KU Leuven Leuven Belgium Department of Pharmacology and Personalised Medicine Faculty of Health Medicine and Life Science Maastricht University Maastricht Netherlands

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body. © Egils Stalidzans et al. 2020 Published b

关键词： Iterative methods

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

Nature Water

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Nature Water 2025年 1-11页

作者： Eva Aßmann Christopher Irrgang Hugues Richard Martin Hölzer Timo Greiner Alexander Ullrich Matthew Wade Shelesh Agrawal Susanne Lackner Fabian Amman Sindy Böttcher Markus Landthaler Emanuel Wyler Serghei Mangul Viorel Munteanu Fotis Psomopoulos Maureen Smith Maria Trofimova Max von Kleist Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London UK Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA USA Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE.

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