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A mixed-order quasicontinuum approach for beam-based architected materials with application to fracture arXiv.cs.CE Pub Date : 2024-03-14 Kevin Kraschewski, Gregory P. Phlipot, Dennis M. Kochmann
Predicting the mechanics of large structural networks, such as beam-based architected materials, requires a multiscale computational strategy that preserves information about the discrete structure while being applicable to large assemblies of struts. Especially the fracture properties of such beam lattices necessitate a two-scale modeling strategy, since the fracture toughness depends on discrete
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Modular parametric PGD enabling online solution of partial differential equations arXiv.cs.CE Pub Date : 2024-03-14 Angelo Pasquale, Mohammad-Javad Kazemzadeh-Parsi, Daniele Di Lorenzo, Victor Champaney, Amine Ammar, Francisco Chinesta
In the present work, a new methodology is proposed for building surrogate parametric models of engineering systems based on modular assembly of pre-solved modules. Each module is a generic parametric solution considering parametric geometry, material and boundary conditions. By assembling these modules and satisfying continuity constraints at the interfaces, a parametric surrogate model of the full
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Improved bass model using sales proportional average for one condition of mono peak curves arXiv.cs.CE Pub Date : 2024-03-13 Ahmad Abu Sleem, Mohammed Alromema, Mohammad A. M. Abdel-Aal
"This study provides a modified Bass model to deal with trend curves for basic issues of relevance to individuals from all over the world, for which we collected 16 data sets from 2004 to 2022 and that are available on Google servers as "google trends". It was discovered that the Bass model did not forecast well for curves that have a mono peak with a sharp decrease to some level then have semi-stable
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Evaluating the Efficiency and Cost-effectiveness of RPB-based CO2 Capture: A Comprehensive Approach to Simultaneous Design and Operating Condition Optimization arXiv.cs.CE Pub Date : 2024-03-13 Howoun Jung, Nohjin Park, Jay H. Lee
Despite ongoing global initiatives to reduce CO2 emissions, implementing large-scale CO2 capture using amine solvents is fraught with economic uncertainties and technical hurdles. The Rotating Packed Bed (RPB) presents a promising alternative to traditional packed towers, offering compact design and adaptability. Nonetheless, scaling RPB processes to an industrial level is challenging due to the nascent
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Towards Code Generation for Octree-Based Multigrid Solvers arXiv.cs.CE Pub Date : 2024-03-12 Richard Angersbach, Sebastian Kuckuck, Harald Köstler
This paper presents a novel method designed to generate multigrid solvers optimized for octree-based software frameworks. Our approach focuses on accurately capturing local features within a domain while leveraging the efficiency inherent in multigrid techniques. We outline the essential steps involved in generating specialized kernels for local refinement and communication routines, integrating on-the-fly
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A time-adaptive finite element phase-field model suitable for rate-independent fracture mechanics arXiv.cs.CE Pub Date : 2024-03-12 Felix Rörentrop, Samira Boddin, Dorothee Knees, Jörn Mosler
The modeling of cracks is an important topic - both in engineering as well as in mathematics. Since crack propagation is characterized by a free boundary value problem (the geometry of the crack is not known beforehand, but part of the solution), approximations of the underlying sharp-interface problem based on phase-field models are often considered. Focusing on a rate-independent setting, these models
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Optimization of Pressure Management Strategies for Geological CO2 Sequestration Using Surrogate Model-based Reinforcement Learning arXiv.cs.CE Pub Date : 2024-03-12 Jungang Chen, Eduardo Gildin, John E. Killough
Injecting greenhouse gas into deep underground reservoirs for permanent storage can inadvertently lead to fault reactivation, caprock fracturing and greenhouse gas leakage when the injection-induced stress exceeds the critical threshold. Extraction of pre-existing fluids at various stages of injection process, referred as pressure management, can mitigate associated risks and lessen environmental impact
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Towards Full Automation of Geometry Extraction for Biomechanical Analysis of Abdominal Aortic Aneurysm; Neural Network-Based versus Classical Methodologies arXiv.cs.CE Pub Date : 2024-03-12 Farah Alkhatib, Mostafa Jamshidian, Donatien Le Liepvre, Florian Bernard, Ludovic Minvielle, Adam Wittek, Karol Miller
In this study we investigated the impact of image segmentation methods on the results of stress computation in the wall of abdominal aortic aneurysms (AAAs). We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Ten different AAA contrast-enhanced computed tomography
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Numerical simulation of individual coil placement - A proof-of-concept study for the prediction of recurrence after aneurysm coiling arXiv.cs.CE Pub Date : 2024-03-11 Julian Schwarting, Fabian Holzberger, Markus Muhr, Martin Renz, Tobias Boeckh-Behrens, Barbara Wohlmuth, Jan Kirschke
Rupture of intracranial aneurysms results in severe subarachnoidal hemorrhage, which is associated with high morbidity and mortality. Neurointerventional occlusion of the aneurysm through coiling has evolved to a therapeutical standard. The choice of the specific coil has an important influence on secondary regrowth requiring retreatment. Aneurysm occlusion was simulated either through virtual implantation
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Performance of Expansive Soil Stabilized with Bamboo Charcoal, Quarry Dust, and Lime for Use as Road Subgrade Material arXiv.cs.CE Pub Date : 2024-03-11 Essizewa Essowedeou Agate, Nyomboi Timothy, Ambassah O. Nathaniel, Ines Ngassam
Expansive soils such as Black Cotton Soils (BCS) present significant challenges for road subgrade construction due to their high plasticity, swelling potential, and low strength. This study explores a triphasic stabilization method using Bamboo Charcoal (BC), Quarry Dust (QD), and Lime (L) to enhance the engineering properties of BCS for rural road applications. Initial soil characterization involved
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When Crypto Economics Meet Graph Analytics and Learning arXiv.cs.CE Pub Date : 2024-03-11 Bingqiao Luo
Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly focuses on leading cryptocurrencies, namely Bitcoin (BTC) and Ethereum (ETH), overlooking the vast diversity among the more than 10,000 cryptocurrency
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RADS : Restricted Anisotropic Diffusion Spectrum model for Axonal Health quantification in Multiple Sclerosis arXiv.cs.CE Pub Date : 2024-03-10 Nand Sharma
Axonal damage is the primary pathological correlate of long-term impairment in multiple sclerosis (MS). Our previous work using our method - diffusion basis spectrum imaging (DBSI) - demonstrated a strong, quantitative relationship between axial diffusivity and axonal damage. In the present work, we develop an extension of DBSI which can be used to quantify the fraction of diseased and healthy axons
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Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities arXiv.cs.CE Pub Date : 2024-03-10 Morgen Pronk
In the evolving field of maintenance and reliability engineering, the organization of equipment into hierarchical structures presents both a challenge and a necessity, directly impacting the operational integrity of industrial facilities. This paper introduces an innovative approach employing machine learning, specifically Long Short-Term Memory (LSTM) models, to automate and enhance the creation and
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Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms arXiv.cs.CE Pub Date : 2024-03-09 Shuwei Zhu, Siying Lv, Kaifeng Chen, Wei Fang, Leilei Cao
The design optimization of ship hull form based on hydrodynamics theory and simulation-based design (SBD) technologies generally considers ship performance and energy efficiency performance as the design objective, which plays an important role in smart design and manufacturing of green ship. An optimal design of sustainable energy system requires multidisciplinary tools to build ships with the least
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FMPAF: How Do Fed Chairs Affect the Financial Market? A Fine-grained Monetary Policy Analysis Framework on Their Language arXiv.cs.CE Pub Date : 2024-03-10 Yayue Deng, Mohan Xu, Yao Tang
The effectiveness of central bank communication is a crucial aspect of monetary policy transmission. While recent research has examined the influence of policy communication by the chairs of the Federal Reserve on various financial variables, much of the literature relies on rule-based or dictionary-based methods in parsing the language of the chairs, leaving nuanced information about policy stance
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Rediscovering the Mullins Effect With Deep Symbolic Regression arXiv.cs.CE Pub Date : 2024-03-08 Rasul Abdusalamov, Jendrik Weise, Mikhail Itskov
The Mullins effect represents a softening phenomenon observed in rubber-like materials and soft biological tissues. It is usually accompanied by many other inelastic effects like for example residual strain and induced anisotropy. In spite of the long term research and many material models proposed in literature, accurate modeling and prediction of this complex phenomenon still remain a challenging
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MambaLithium: Selective state space model for remaining-useful-life, state-of-health, and state-of-charge estimation of lithium-ion batteries arXiv.cs.CE Pub Date : 2024-03-08 Zhuangwei Shi
Recently, lithium-ion batteries occupy a pivotal position in the realm of electric vehicles and the burgeoning new energy industry. Their performance is heavily dependent on three core states: remaining-useful-life (RUL), state-of-health (SOH), and state-of-charge (SOC). Given the remarkable success of Mamba (Structured state space sequence models with selection mechanism and scan module, S6) in sequence
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Survival probability of structures under fatigue: a data-based approach arXiv.cs.CE Pub Date : 2024-03-08 Francois-Baptiste Cartiaux, Frederic Legoll, Alex Libal, Julien Reygner
This article addresses the probabilistic nature of fatigue life in structures subjected to cyclic loading with variable amplitude. Drawing on the formalisation of Miner's cumulative damage rule that we introduced in the recent article [Cartiaux, Ehrlacher, Legoll, Libal and Reygner, Prob. Eng. Mech. 2023], we apply our methodology to estimate the survival probability of an industrial structure using
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Modeling of progressive high-cycle fatigue in composite laminates accounting for local stress ratios arXiv.cs.CE Pub Date : 2024-03-08 P. Hofman, F. P. van der Meer, L. J. Sluys
A numerical framework for simulating progressive failure under high-cycle fatigue loading is validated against experiments of composite quasi-isotropic open-hole laminates. Transverse matrix cracking and delamination are modeled with a mixed-mode fatigue cohesive zone model, covering crack initiation and propagation. Furthermore, XFEM is used for simulating transverse matrix cracks and splits at arbitrary
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A Model Hierarchy for Predicting the Flow in Stirred Tanks with Physics-Informed Neural Networks arXiv.cs.CE Pub Date : 2024-03-07 Veronika Trávníková, Daniel Wolff, Nico Dirkes, Stefanie Elgeti, Eric von Lieres, Marek Behr
This paper explores the potential of Physics-Informed Neural Networks (PINNs) to serve as Reduced Order Models (ROMs) for simulating the flow field within stirred tank reactors (STRs). We solve the two-dimensional stationary Navier-Stokes equations within a geometrically intricate domain and explore methodologies that allow us to integrate additional physical insights into the model. These approaches
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Modeling Methane Intensity of Oil and Gas Upstream Activities by Production Profile arXiv.cs.CE Pub Date : 2024-03-07 Quentin Peyle, Imene Ben Rejeb-Mzah, Baptiste Piofret, Antoine Benoit, Alexandre d'Aspremont, Adil El Yaalaoui
We propose a methodology for modelling methane intensities of Oil and Gas upstream activities for different production profiles with diverse combinations of region of operation and production volumes associated. This methodology leverages different data sources, including satellite measurements and public estimates of methane emissions but also country-level oil and gas production data and company
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Effect of turbulent diffusion in modeling anaerobic digestion arXiv.cs.CE Pub Date : 2024-03-07 Jeremy Z. Yan, Prashant Kumar, Wolfgang Rauch
In this study, the impact of turbulent diffusion on mixing of biochemical reaction models is explored by implementing and validating different models. An original codebase called CHAD (Coupled Hydrodynamics and Anaerobic Digestion) is extended to incorporate turbulent diffusion and validate it against results from OpenFOAM with 2D Rayleigh-Taylor Instability and lid-driven cavity simulations. The models
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Blockchain and Carbon Markets: Standards Overview arXiv.cs.CE Pub Date : 2024-03-06 Pedro Baiz
The increasing significance of sustainability considerations within both public spheres (such as policies and regulations) and private sectors (including voluntary commitments by major multinational corporations) underscores the imperative to harness cutting-edge technological advancements. This is essential to ensure that the momentum of this trend translates into tangible outcomes, thwarting phenomena
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Multi-time-step coupling of peridynamics and classical continuum mechanics for dynamic brittle fracture arXiv.cs.CE Pub Date : 2024-03-06 Zhong Jiandong, Han Fei, Du Zongliang, Guo Xu
Peridynamics (PD), as a nonlocal theory, is well-suited for solving problems with discontinuities, such as cracks. However, the nonlocal effect of peridynamics makes it computationally expensive for dynamic fracture problems in large-scale engineering applications. As an alternative, this study proposes a multi-time-step (MTS) coupling model of PD and classical continuum mechanics (CCM) based on the
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Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences arXiv.cs.CE Pub Date : 2024-03-06 Jin Li, Kleanthis Malialis, Marios M. Polycarpou
In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept
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Reducing computational effort in topology optimization considering the deformation in additive manufacturing arXiv.cs.CE Pub Date : 2024-03-05 Takao Miki
Integrating topology optimization and additive manufacturing (AM) technology can facilitate innovative product development. However, laser powder bed fusion, which is the predominant method in metal AM, can lead to issues such as residual stress and deformation. Recently, topology optimization methods considering these stresses and deformations have been proposed; however, they suffer from challenges
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A Novel Shortest Path Query Algorithm Based on Optimized Adaptive Topology Structure arXiv.cs.CE Pub Date : 2024-03-04 Xiao Fang, Xuyang Song, Jiyuan Ma, Guanhua Liu, Shurong Pang, Wenbo Zhao, Cong Cao, Ling Fan
Urban rail transit is a fundamental component of public transportation, however, commonly station-based path search algorithms often overlook the impact of transfer times on search results, leading to decreased accuracy. To solve this problem, this paper proposes a novel shortest path query algorithm based on adaptive topology optimization called the Adaptive Topology Extension Road Network Structure
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Monitoring the Seismic Behavior of a Scaled RC Frame with Intermediate Ductility in a Shaking Table Test arXiv.cs.CE Pub Date : 2024-03-03 Mohammad Vasef, Mohammad Sadegh Marefat, Sina Shid-Moosavi, Peng "Patrick" Sun
One of the commonly used seismic force-resisting systems in structures is Reinforced Concrete (RC) Intermediate Moment Frames (IMF). Although using the IMF is not allowed in high seismic hazard zones according to ASCE 7-10, it is permitted in both Iran's 2800 Seismic Standard and New Zealand's Seismic Code. This study investigates the seismic behavior of a reinforced concrete IMF subjected to earthquake
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An Authentic Algorithm for Ciphering and Deciphering Called Latin Djokovic arXiv.cs.CE Pub Date : 2024-03-03 Diogen Babuc
The question that is a motivation of writing is how many devote themselves to discovering something in the world of science where much is discerned and revealed, but at the same time, much is unknown. The insightful elements of this algorithm are the ciphering and deciphering algorithms of Playfair, Caesar, and Vigenere. Only a few of their main properties are taken and modified, with the aim of forming
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Optimization decision model of vegetable stock and pricing based on TCN-Attention and genetic algorithm arXiv.cs.CE Pub Date : 2024-03-03 Linhan Xia, Jinyuan Zhang, Bohan Wen
With the expansion of operational scale of supermarkets in China, the vegetable market has grown considerably. The decision-making related to procurement costs and allocation quantities of vegetables has become a pivotal factor in determining the profitability of supermarkets. This paper analyzes the relationship between pricing and allocation faced by supermarkets in vegetable operations. Optimization
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Accelerating Hydrodynamic Fabrication of Microstructures using Deep Neural Networks arXiv.cs.CE Pub Date : 2024-03-02 Nicholus R. Clinkinbeard, Reza Montazami, Nicole N. Hashemi
Manufacturing of microstructures using a microfluidic device is a largely empirical effort due to the multi-physical nature of the fabrication process. As such, models are desired that will predict microstructure performance characteristics (e.g., size, porosity, and stiffness) based on known inputs, such as sheath and core fluid flow rates. Potentially more useful is the prospect of inputting desired
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Protein Multimer Structure Prediction via Prompt Learning arXiv.cs.CE Pub Date : 2024-02-29 Ziqi Gao, Xiangguo Sun, Zijing Liu, Yu Li, Hong Cheng, Jia Li
Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the multimer structure prediction~(MSP) can be well handled in a step-wise assembly fashion using provided dimer structures and predicted protein-protein interactions~(PPIs). However, due to the biological gap in the formation of
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Physics-based block preconditioning for mixed-dimensional beam-solid interaction arXiv.cs.CE Pub Date : 2024-02-28 Max Firmbach, Ivo Steinbrecher, Alexander Popp, Matthias Mayr
This paper presents a scalable physics-based block preconditioner for mixed-dimensional models in beam-solid interaction and their application in engineering. In particular, it studies the linear systems arising from a regularized mortar-type approach for embedding geometrically exact beams into solid continua. Due to the lack of block diagonal dominance of the arising 2 x 2 block system, an approximate
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A highly efficient computational approach for part-scale microstructure predictions in Ti-6Al-4V additive manufacturing arXiv.cs.CE Pub Date : 2024-02-27 Sebastian D. Proell, Julian Brotz, Martin Kronbichler, Wolfgang A. Wall, Christoph Meier
Fast and efficient simulations of metal additive manufacturing (AM) processes are highly relevant to exploring the full potential of this promising manufacturing technique. The microstructure composition plays an important role in characterizing the part quality and deriving mechanical properties. When complete parts are simulated, one often needs to resort to strong simplifications such as layer-wise
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Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences arXiv.cs.CE Pub Date : 2024-02-23 Natalia L. Rubio, Luca Pegolotti, Martin R. Pfaller, Eric F. Darve, Alison L. Marsden
Reduced-order models (ROMs) allow for the simulation of blood flow in patient-specific vasculatures without the high computational cost and wait time associated with traditional computational fluid dynamics (CFD) models. Unfortunately, due to the simplifications made in their formulations, ROMs can suffer from significantly reduced accuracy. One common simplifying assumption is the continuity of static
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On Languaging a Simulation Engine arXiv.cs.CE Pub Date : 2024-02-26 Han Liu, Liantang Li
Language model intelligence is revolutionizing the way we program materials simulations. However, the diversity of simulation scenarios renders it challenging to precisely transform human language into a tailored simulator. Here, using three functionalized types of language model, we propose a language-to-simulation (Lang2Sim) framework that enables interactive navigation on languaging a simulation
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Design and Optimization of Functionally-graded Triangular Lattices for Multiple Loading Conditions arXiv.cs.CE Pub Date : 2024-02-23 Junpeng Wang, Rüdiger Westermann, Xifeng Gao, Jun Wu
Aligning lattices based on local stress distribution is crucial for achieving exceptional structural stiffness. However, this aspect has primarily been investigated under a single load condition, where stress in 2D can be described by two orthogonal principal stress directions. In this paper, we introduce a novel approach for designing and optimizing triangular lattice structures to accommodate multiple
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Open Meshed Anatomy: Towards a comprehensive finite element hexahedral mesh derived from open atlases arXiv.cs.CE Pub Date : 2024-02-22 Andy Trung Huynh, Benjamin Zwick, Michael Halle, Adam Wittek, Karol Miller
Computational simulations using methods such as the finite element (FE) method rely on high-quality meshes for achieving accurate results. This study introduces a method for creating a high-quality hexahedral mesh using the Open Anatomy Project's brain atlas. Our atlas-based FE hexahedral mesh of the brain mitigates potential inaccuracies and uncertainties due to segmentation - a process that often
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Theory and Computation of Substructure Characteristic Modes arXiv.cs.CE Pub Date : 2024-02-22 Mats Gustafsson, Lukas Jelinek, Miloslav Capek, Johan Lundgren, Kurt Schab
The problem of substructure characteristic modes is reformulated using a scattering matrix-based formulation, generalizing subregion characteristic mode decomposition to arbitrary computational tools. It is shown that the scattering formulation is identical to the classical formulation based on the background Green's function for lossless systems. The scattering formulation, however, opens a variety
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Multigrid on unstructured meshes with regions of low quality cells arXiv.cs.CE Pub Date : 2024-02-20 Yuxuan Chen, Garth N. Wells
The convergence of multigrid methods degrades significantly if a small number of low quality cells are present in a finite element mesh, and this can be a barrier to the efficient and robust application of multigrid on complicated geometric domains. The degraded performance is observed also if intermediate levels in a non-nested geometric multigrid problem have low quality cells, even when the fine
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The matrix-free macro-element hybridized Discontinuous Galerkin method for steady and unsteady compressible flows arXiv.cs.CE Pub Date : 2024-02-17 Vahid Badrkhani, Marco F. P. ten Eikelder, Rene R. Hiemstra, Dominik Schillinger
The macro-element variant of the hybridized discontinuous Galerkin (HDG) method combines advantages of continuous and discontinuous finite element discretization. In this paper, we investigate the performance of the macro-element HDG method for the analysis of compressible flow problems at moderate Reynolds numbers. To efficiently handle the corresponding large systems of equations, we explore several
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Linear and Non-Linear Models for Master Scheduling of Dynamic Resources Product Mix arXiv.cs.CE Pub Date : 2024-02-17 Ayman R. Mohammed, Ahmad Abu Sleem, Mohammad A. M. Abdel-Aal
The literature on master production scheduling for product mix problems under the Theory of Constraints (TOC) was considered by many previous studies. Most studies assume a static resources availability. In this study, the raw materials supplied to the manufacturer is considered as dynamic depending on the results of the problem. Thus, an integer linear heuristic, an integer non-linear optimization
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An energy-based material model for the simulation of shape memory alloys under complex boundary value problems arXiv.cs.CE Pub Date : 2024-02-16 C. Erdogan, T. Bode, P. Junker
Shape memory alloys are remarkable 'smart' materials used in a broad spectrum of applications, ranging from aerospace to robotics, thanks to their unique thermomechanical coupling capabilities. Given the complex properties of shape memory alloys, which are largely influenced by thermal and mechanical loads, as well as their loading history, predicting their behavior can be challenging. Consequently
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Mitigating subjectivity and bias in AI development indices: A robust approach to redefining country rankings arXiv.cs.CE Pub Date : 2024-02-15 Betania Silva C Campello, Guilherme Dean Pelegrina, Renata Pelissari, Ricardo Suyama, Leonardo Tomazeli Duarte
Countries worldwide have been implementing different actions national strategies for Artificial Intelligence (AI) to shape policy priorities and guide their development concerning AI. Several AI indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices combine multiple indicators using linear additive methods
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Validation of homogenized finite element models of human metastatic vertebrae using digital volume correlation arXiv.cs.CE Pub Date : 2024-02-15 Chiara Garavelli, Alessandra Aldieri, Marco Palanca, Enrico Dall'Ara, Marco Viceconti
The incidence of vertebral fragility fracture is increased by the presence of preexisting pathologies such as metastatic disease. Computational tools could support the fracture prediction and consequently the decision of the best medical treatment. Anyway, validation is required to use these tools in clinical practice. To address this necessity, in this study subject-specific homogenized finite element
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Multi-vertebral CT-based FE models implementing linear isotropic population-based material properties for the intervertebral discs cannot accurately predict strains arXiv.cs.CE Pub Date : 2024-02-15 Chiara Garavelli, Alessandra Aldieri, Marco Palanca, Luca Patruno, Marco Viceconti
Vertebral fractures prediction in clinics lacks of accuracy. The most used scores have limitations in distinguishing between subjects at risk or not. Finite element (FE) models generated from computed tomography (CT) of these patients may improve the predictive capability. Many models have already been proposed but the most of them considered the single vertebral body, excluding from the analysis the
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Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations arXiv.cs.CE Pub Date : 2024-02-14 Roberto Perera, Vinamra Agrawal
Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to simulate complex multiphysics problems with accelerated performance times. However, mesh-based GNNs require a large number of message-passing (MP) steps and suffer from over-smoothing for problems involving very fine mesh. In this work, we develop a multiscale mesh-based GNN framework mimicking a conventional iterative multigrid
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Joint Source-Channel Coding for Wireless Image Transmission: A Deep Compressed-Sensing Based Method arXiv.cs.CE Pub Date : 2024-02-11 Mohammad Amin Jarrahi, Eirina Bourtsoulatze, Vahid Abolghasemi
Nowadays, the demand for image transmission over wireless networks has surged significantly. To meet the need for swift delivery of high-quality images through time-varying channels with limited bandwidth, the development of efficient transmission strategies and techniques for preserving image quality is of importance. This paper introduces an innovative approach to Joint Source-Channel Coding (JSCC)
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Nonlinear electro-elastic finite element analysis with neural network constitutive models arXiv.cs.CE Pub Date : 2024-02-10 Dominik K. Klein, Rogelio Ortigosa, Jesús Martínez-Frutos, Oliver Weeger
In the present work, the applicability of physics-augmented neural network (PANN) constitutive models for complex electro-elastic finite element analysis is demonstrated. For the investigations, PANN models for electro-elastic material behavior at finite deformations are calibrated to different synthetically generated datasets, including an analytical isotropic potential, a homogenised rank-one laminate
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Formalizing Automated Market Makers in the Lean 4 Theorem Prover arXiv.cs.CE Pub Date : 2024-02-08 Daniele Pusceddu, Massimo Bartoletti
Automated Market Makers (AMMs) are an integral component of the decentralized finance (DeFi) ecosystem, as they allow users to exchange crypto-assets without the need for trusted authorities or external price oracles. Although these protocols are based on relatively simple mechanisms, e.g., to algorithmically determine the exchange rate between crypto-assets, they give rise to complex economic behaviours
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A plastic correction algorithm for full-field elasto-plastic finite element simulations : critical assessment of predictive capabilities and improvement by machine learning arXiv.cs.CE Pub Date : 2024-02-09 Abhishek Palchoudhary, Simone Peter, Vincent Maurel, Cristian Ovalle, Pierre Kerfriden
This paper introduces a new local plastic correction algorithm developed to accelerate finite element simulations for structures with elasto-plastic constitutive laws. The proposed method belongs to the category of generalized multiaxial Neuber-type methods enabled by pointwise proportional evolution rules. The algorithm numerically integrates J2 plasticity laws as a function of the finite element
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Energy-based PINNs for solving coupled field problems: concepts and application to the optimal design of an induction heater arXiv.cs.CE Pub Date : 2024-02-09 Marco Baldan, Paolo Di Barba
Physics-informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residual of the governing equations, there are energy-based approaches that take a different path by minimizing the variational energy of the model. We show that
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Relative frequencies of constrained events in stochastic processes: An analytical approach arXiv.cs.CE Pub Date : 2024-02-09 S. Rusconi, E. Akhmatskaya, D. Sokolovski, N. Ballard, J. C. de la Cal
The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among the most common approaches for studying stochastic processes. They rely on knowledge of interevent probability density functions (PDFs) and on information about dependencies between all possible events. Analytical representations of a PDF are difficult to specify in advance, in many real life applications
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Reducing model complexity by means of the Optimal Scaling: Population Balance Model for latex particles morphology formation arXiv.cs.CE Pub Date : 2024-02-09 Simone Rusconi, Christina Schenk, Arghir Zarnescu, Elena Akhmatskaya
Rational computer-aided design of multiphase polymer materials is vital for rapid progress in many important applications, such as: diagnostic tests, drug delivery, coatings, additives for constructing materials, cosmetics, etc. Several property predictive models, including the prospective Population Balance Model for Latex Particles Morphology Formation (LPMF PBM), have already been developed for
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Maximizing NFT Incentives: References Make You Rich arXiv.cs.CE Pub Date : 2024-02-09 Guangsheng Yu, Qin Wang, Caijun Sun, Lam Duc Nguyen, H. M. N. Dilum Bandara, Shiping Chen
In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across an unexpected finding. That is, the current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. We propose, analyze
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Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval arXiv.cs.CE Pub Date : 2024-02-09 Arne Wulff, Boyang Chen, Matthew Steinberg, Yinglu Tang, Matthias Möller, Sebastian Feld
As with many tasks in engineering, structural design frequently involves navigating complex and computationally expensive problems. A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable task, due to an exponentially large configuration space and non-linear constraints. The rapidly developing field of quantum computation may offer novel approaches
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Towards participatory multi-modeling for policy support across domains and scales: a systematic procedure for integral multi-model design arXiv.cs.CE Pub Date : 2024-02-09 Vittorio NespecaComputational Science Lab - University of AmsterdamPOLDER - Institute for Advanced Study - University of AmsterdamFaculty of Technology Policy and Management - Delft University of Technology, Rick QuaxComputational Science Lab - University of AmsterdamPOLDER - Institute for Advanced Study - University of Amsterdam, Marcel G. M. Olde RikkertDepartment Geriatrics - Radboud University
Policymaking for complex challenges such as pandemics necessitates the consideration of intricate implications across multiple domains and scales. Computational models can support policymaking, but a single model is often insufficient for such multidomain and scale challenges. Multi-models comprising several interacting computational models at different scales or relying on different modeling paradigms
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Trustful Coopetitive Infrastructures for the New Space Exploration Era arXiv.cs.CE Pub Date : 2024-02-08 Renan Lima BaimaFINATRAX - Digital Financial Services and Cross-Organisational Digital Transformations, Loïck ChovetSpaceR - Space Robotics, SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Eduard HartwichFINATRAX - Digital Financial Services and Cross-Organisational Digital Transformations, Abhishek BeraSpaceR - Space Robotics, SnT - Interdisciplinary Centre
In the new space economy, space agencies, large enterprises, and start-ups aim to launch space multi-robot systems (MRS) for various in-situ resource utilization (ISRU) purposes, such as mapping, soil evaluation, and utility provisioning. However, these stakeholders' competing economic interests may hinder effective collaboration on a centralized digital platform. To address this issue, neutral and
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Shape Optimization of Eigenfrequencies in MEMS Gyroscopes arXiv.cs.CE Pub Date : 2024-02-08 Daniel Schiwietz, Marian Hörsting, Eva Maria Weig, Peter Degenfeld-Schonburg, Matthias Wenzel
Microelectromechanical systems (MEMS) gyroscopes are widely used in consumer and automotive applications. They have to fulfill a vast number of product requirements which lead to complex mechanical designs of the resonating structure. Arriving at a final design is a cumbersome process that relies heavily on human experience in conjunction with design optimization methods. In this work, we apply node-based
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I-FENN with Temporal Convolutional Networks: expediting the load-history analysis of non-local gradient damage propagation arXiv.cs.CE Pub Date : 2024-02-08 Panos Pantidis, Habiba Eldababy, Diab Abueidda, Mostafa E. Mobasher
In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors, can efficiently simulate the entire loading history of non-local gradient damage propagation. To achieve this goal, we first adopt a Temporal Convolutional Network (TCN) as the neural network of choice to capture the history-dependent evolution of