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Advanced Modeling and Simulation in Engineering Sciences
Scopus
ISSN 2213-7467 · SpringerOpen · 247 artikel di indeks
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Artikel Jurnal
Predicting rolling shear failure of cross-laminated panels using a multiscale domain decomposition approach
Abstract Cross-laminated timber (CLT) is gaining popularity as a sustainable building material due to its favorable structural performance and low environmental impact. This study introduces a multiscale domain decomposition method, known as the LaTIn strategy, to predict the out-of-plane behavior…
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A robust population mean estimation method in stratified sampling using auxiliary attributes under real and simulated populations
Abstract This study proposes a novel improved ratio-type estimator for efficient estimation of the population mean in stratified sampling by integrating auxiliary attribute information through a unified nonlinear framework. Unlike conventional ratio, product, and exponential-type estimators, the…
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Artikel Jurnal
Generative surrogate modelling for the efficient design optimization of an RF component
Abstract Accurate and efficient design of millimeter-wave components is critical for sixth-generation (6 G) wireless systems, particularly at the D-band where low-loss interconnects are required. Vertically aligned carbon-nanotube (VACNT)–based air-filled waveguides (AFWGs) have recently been…
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Artikel Jurnal
Remaining useful life of power transformers using efficient surrogates and deep learning techniques
Abstract The assessment of remaining useful life (RUL) in power transformers (PTs) is critical, given their essential role in the reliable distribution of electrical energy. According to international standards, the RUL is primarily influenced by the temperature of the hotspot (HST) located near…
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Development of a digital twin of a powertrain for efficiency estimations with machine learning and physical modeling
Abstract Accurate prediction of the efficiency of electromechanical powertrains is crucial for the development of electric vehicles. Until now, this has been done through time-consuming and costly test bench investigations in late development phases. To address this, digital modeling and…
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Artikel Jurnal
Artificial intelligence-driven physics-informed feature engineering for multiaxial fatigue life prediction of metallic materials with Bayesian optimization
Abstract Conventional fatigue life prediction methods struggle with multiaxial and non-proportional loading conditions, yet accurate prediction is critical for component reliability in aerospace, automotive, and structural applications. This paper presents a novel AI framework with physics-informed…
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A comprehensive study on estimating the primary crack spacing of flexural reinforced concrete components using machine learning techniques
Abstract Accurate estimation of primary crack spacing in flexural reinforced concrete components is essential for serviceability assessment and durability-related decision making. This study evaluates eleven regression-based machine learning models, combined with nine preprocessing strategies, to…
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Artikel Jurnal
Hybrid physics: data framework for real-time forecasting of atmospheric pollutants
Abstract Forecasting the spatio-temporal evolution of atmospheric pollutants is a critical challenge for air quality management and public health. Traditional approaches based on Gaussian, Lagrangian, and Eulerian frameworks rely on advection–diffusion equations and meteorological forcing, while…
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Artikel Jurnal
A new framework for generative design, real-time prediction, and inverse design optimization: application to microstructure
Abstract Optimizing composite materials for specific applications requires the ability to design microstructures that meet targeted mechanical performance, as well as to predict their behavior efficiently. This study introduces a new framework based on Rank Reduction Autoencoders (RRAEs) to enable…
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Transient structural analysis with proper generalized decomposition
Abstract The design of high-temperature rotating machinery, such as aero engines and gas turbines, requires precise stress evaluation during transient operating conditions to ensure structural reliability. Despite advances in computational techniques, transient structural analysis remains…
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A black box variational inference scheme for inverse problems with demanding physics-based models
Abstract Bayesian methods are particularly effective for addressing inverse problems due to their ability to manage uncertainties inherent in the inference process. However, employing these methods with costly forward models poses significant challenges, especially in the context of…
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Artikel Jurnal
Two-stage ROM: a hybrid reduced order model for offline and online efficiency
Abstract Physics-based reduced-order models are one of the key enablers for improving the predictive capabilities of digital twins, offering a balance between computational efficiency and physical fidelity. However, their efficiency for nonlinear problems remains a challenge because evaluating…
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Artikel Jurnal
Foam property prediction and inception via a hybrid machine learning and model order reduction framework
Abstract Novel methodologies in machine learning enable non-destructive prediction of mechanical properties through the use of either material components or material imaging. Predictive technologies are continuously being developed in the field of mechanical and materials engineering. However, when…
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Hybrid machine learning and physical modeling of feedstock deformation during robotic 3D printing of continuous fiber thermoplastic composites
Abstract Feedstock deformation during 3D printing of continuous fiber composites is a critical challenge in path planning and a main driver in the generation of manufacturing defects. The proposed work addressed the feedstock deformation during the deposition through several experimental and…
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Artikel Jurnal
Multiphysics modelling and interface optimization of PEDOT:PSS OECTs for stable and sensitive biosensing
Abstract The work presents a detailed analytical and experimental study of the interface analysis of Organic Electrochemical Transistors (OECTs) used for advanced biosensing applications. The focus is on understanding how the gate–electrolyte and channel–electrolyte interfaces affect the overall…
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A compact microwave CSRR resonator for non-invasive glucose sensing enhanced by machine learning
Abstract This paper presents a new, compact shape of a planar microwave resonator sensor for non-invasive blood glucose monitoring, enabling effective diabetes management. The proposed sensor is based on four cells of compact, irregularly curved Coupled Split Ring Resonators (CSRRs) printed on an…
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Artikel Jurnal
Structural natural frequency-embedded Fourier feature physics-informed neural networks for earthquake dynamic response prediction
Abstract Conventional Physics-Informed Neural Networks (PINNs) with fully connected architectures have shown potential in solving differential equations, yet their inherent spectral bias limits accurate reconstruction of high-frequency components—critical for structural dynamics under broadband…
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Artikel Jurnal
A tail-adjusted reliability index for non-Gaussian structural systems
Abstract The conventional reliability index, although commonly adopted in structural engineering, is constructed under Gaussian assumptions and remains largely insensitive to distributional tail characteristics. As a result, it may significantly underestimate the probability of failure when input…
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Artikel Jurnal
An extension of the modified constitutive relation error concept for the robust multi-physics offshore wind turbine model calibration from in situ data
Abstract In order to address health monitoring issues on offshore wind turbines, while taking into account engineering know-how and constraints, we propose a robust model identification procedure based on reliability of information for such structures. The procedure leans on the modified…
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Artikel Jurnal
A methodology for the automated estimation of footprint-derived seismic behaviour modifiers in building exposure assessment
Abstract Comprehensive and accurate Geospatial Exposure Databases (GED) are fundamental for seismic risk assessment, but their creation and maintenance is a major challenge in regions with limited or non-existent cadastral data. GEDs store the characterization of seismic exposure attributes at the…