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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 293)
Statistics in Medicine     Hybrid Journal   (Followers: 156)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 78, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Biometrics     Hybrid Journal   (Followers: 51)
Sociological Methods & Research     Hybrid Journal   (Followers: 50)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 42, SJR: 3.664, CiteScore: 2)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 39)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 27)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 25)
Journal of Applied Statistics     Hybrid Journal   (Followers: 22)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Computational Statistics     Hybrid Journal   (Followers: 17)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Risk Management     Hybrid Journal   (Followers: 16)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Stata Journal     Full-text available via subscription   (Followers: 10)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Handbook of Statistics     Full-text available via subscription   (Followers: 9)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Current Research in Biostatistics     Open Access   (Followers: 9)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Argumentation et analyse du discours     Open Access   (Followers: 8)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Significance     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
TEST     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  

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Engineering With Computers
Journal Prestige (SJR): 0.485
Citation Impact (citeScore): 2
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1435-5663 - ISSN (Online) 0177-0667
Published by Springer-Verlag Homepage  [2468 journals]
  • A nodal-integration-based finite element method for solving steady-state
           nonlinear problems in the loading’s comoving frame

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      Abstract: Abstract Many thermomechanical processes, such as rolling, turning, grinding, welding or additive manufacturing, involve either a material flowing through a fixed load system or a heat source moving with respect to the material. In many situations, these processes involve a constant speed translational, rotational or helical movement of the loading with respect to the material so that a (quasi-) steady thermo-mechanical state is achieved quickly. Classical Lagrangian steady state finite element simulation of these processes in the material’s frame is a heavy task requiring large meshes refined all along the load path. This article presents a nodal-integration-based finite element method for solving transient and steady-state elastoplastic problems associated with these processes. The simulation is carried out step by step in a frame linked to the loading. As the nodes of the mesh do not represent material points, the computation procedure requires determining the position at the previous time step of the material point associated with each node (anterior point) in order to perform the time-integration of the constitutive equations. The anterior points are located anywhere in the mesh and therefore interpolation techniques are required to get the previous mechanical state there. As all the mechanical variables are calculated at nodes with the method proposed, this approach makes the interpolation more straightforward. Applications to 3D forming and welding are presented to illustrate the efficiency of the proposed method. The results of finite element simulations in the frame tied to the loading are compared to those of Lagrangian calculations simulating the load motion in the material’s frame. The applications demonstrate that the proposed model can significantly accelerate simulations, achieving a maximum acceleration of around 40 in 3D forming and about 4 in welding. These results highlight the substantial efficiency improvements enabled by the proposed method.
      PubDate: 2024-08-23
       
  • GRNN-based cascade ensemble model for non-destructive damage state
           identification: small data approach

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      Abstract: Abstract Assessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.
      PubDate: 2024-08-21
       
  • Transferring melt pool knowledge between multiple materials in
           laser-directed energy deposition via Gaussian process regression

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      Abstract: Abstract Laser-directed energy deposition (L-DED) enables the creation of near-net-shape parts with location-specific materials, repair of machine components, and addition of features to existing parts. However, gathering sufficient experimental L-DED data to establish process maps is challenging especially when expensive materials are being investigated. Despite the interest in data-driven modeling for developing such maps, few studies have considered reusing knowledge across multiple materials including uncertainty quantification (UQ). To address this, knowledge transfer methods based on Gaussian process (GP) are proposed. Melt pool data for SS316L and IN718 are used to emulate data-rich and data-scarce conditions, respectively. Three avenues are explored: (i) mixing the data of both materials to train a single GP regression model (the mixed-input model), (ii) relation-based transfer learning (RB-TL) model, and (iii) multi-fidelity GP-based transfer learning (MFGP-TL) model. Results show that the mixed-input model outperforms the baseline or no-transfer model under data-deficient conditions. Compared to the baseline model, the RB-TL model exhibits a general improvement in accuracy and confidence while consuming the least computation time among all proposed models. The MFGP-TL model achieves the best performance, which is only half the error and standard deviation observed for the RB-TL model, albeit resulting in longer computation times. Finally, the proposed transfer learning models, when used on experimental data obtained from the literature, show 22–31% and 24–40% improvement over the baseline model for IN718 and IN625, respectively. This work, therefore, facilitates data- and cost-effective UQ-based knowledge transfer in reconstructing process maps in L-DED.
      PubDate: 2024-08-20
       
  • A node-based consistent non-conforming gradient smoothing scheme for
           highly efficient Galerkin meshfree formulation

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      Abstract: Abstract The stabilized conforming nodal integration (SCNI) is currently widely employed in Galerkin meshfree formulation. A key ingredient of SCNI is the strain or gradient smoothing defined within a set of conforming nodal representative domains, which usually are formed by the auxiliary points in addition to the meshfree nodes. Nonetheless, these auxiliary points may significantly increase the storage requirement and computational cost of SCNI, in comparison with the direct nodal integration. In order to address this issue, a purely node-based consistent non-conforming gradient smoothing (CNGS) scheme is proposed herein to accelerate the Galerkin meshfree computation. In the proposed method, only the meshfree nodes are employed to construct overlapping and non-conforming nodal representative domains, which are then adopted for the nodal gradient smoothing operation. However, unlike the existing non-conforming gradient smoothing algorithms that commonly violate the integration consistency, the proposed method maintains the desirable integration consistency through a proportional separation between the nodal gradient smoothing domains and the nodal integration domains, which essentially ensures the meshfree solution accuracy. Meanwhile, due to the absence of auxiliary points in the gradient smoothing evaluation, the computational efficiency is substantially improved by the proposed method of CNGS compared with SCNI. The effectiveness of the proposed methodology is well demonstrated by numerical results.
      PubDate: 2024-08-17
       
  • A discontinuous piecewise polynomial generalized moving least squares
           scheme for robust finite element analysis on arbitrary grids

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      Abstract: Abstract A variational approach is developed with a meshless discretization to enable accurate and robust numerical simulation of partial differential equations for meshes that are of poor quality. Traditional finite element methods use the mesh to both discretize the geometric domain and to define the finite element shape functions. The latter creates a dependence between the quality of the mesh and the properties of the finite element basis that may adversely affect the accuracy of the discretized problem. We propose a new approach for defining finite element shape functions that breaks this dependence and separates mesh quality from the discretization quality, which we call discontinuous piecewise polynomial generalized moving least squares (DPP-GMLS). At the core of the approach is a meshless definition of the shape functions, which limits the purpose of the mesh to representing the geometric domain and integrating the basis functions without having any role in their approximation quality. The resulting non-conforming space can be utilized within a standard discontinuous Galerkin framework, providing a rigorous foundation for solving partial differential equations on low-quality meshes. We present a collection of numerical experiments demonstrating our approach in a wide range of settings: strongly coercive elliptic problems, linear elasticity in the compressible regime, and the stationary Stokes problem. We demonstrate convergence for all problems and stability for element pairs for problems which usually require inf-sup compatibility for conforming methods, also referring to a minor modification possible through the symmetric interior penalty Galerkin framework for stabilizing element pairs that would otherwise be traditionally unstable. Mesh robustness is particularly critical for elasticity, and we provide an example that our approach provides a greater than 5 \(\times\) improvement in accuracy and allows for taking an 8 \(\times\) larger stable timestep for a highly deformed mesh, compared to the continuous Galerkin finite element method.
      PubDate: 2024-08-16
       
  • Extraction of surface quad layouts from quad layout immersions:
           application to an isogeometric model of car crash

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      Abstract: Abstract Extraction of quadrilateral layouts of surfaces is essential for surface rebuilding using splines, semi-structured bilinear quadrilateral mesh extraction, and texture mapping. Layout generation using integer grid based techniques on triangulated meshes have received particular attention for generation of well-structured layouts. In this work, we reiterate a generalization of integer grid parameterizations in which only topological constraints between singularities are necessary to ensure a valid quadrilateral parameterization (and specifically, the integral curves emanating from singularities are of finite length). This generalized representation is motivated by carefully discussing pros and cons of both integer grid and topologically constrained parameterization methods. A computational framework for extracting a quadrilateral layout from a valid input immersion is then presented, which will work for any parameterization that induces a valid quadrilateral layout. Results demonstrate the validity and the potential of the proposed computational framework on a variety of geometries. The proposed extraction framework is ultimately used to reconstruct the body-in-white of a 1996 Dodge Neon as a set of analysis-suitable bicubic B-splines, which are then used in the first known body-in-white crash analysis using boundary-conforming splines, demonstrating that the reconstruction method is viable for industrial use.
      PubDate: 2024-08-14
       
  • Image-based modeling of coupled electro-chemo-mechanical behavior of
           Li-ion battery cathode using an interface-modified reproducing kernel
           particle method

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      Abstract: Abstract An interface-modified reproducing kernel particle method (IM-RKPM) is introduced in this work to allow for a direct model construction from image pixels of heterogeneous polycrystalline Li-ion battery microstructures. The interface-modified reproducing kernel (IM-RK) approximation is constructed through scaling of a kernel function by a regularized distance function in conjunction with strategic placement of interface node locations. This leads to RK shape functions with either weak or strong discontinuities across material interfaces, suitable for modeling various interface mechanics. With the placement of a triple junction node and distance-based scaling of kernel functions, the resulting IM-RK shape function also possesses proper discontinuities at the triple junctions. This IM-RK approximation effectively remedies the well-known Gibb’s oscillation in the smooth approximation of discontinuities. Different from the conventional meshfree approaches for interface discontinuities, this IM-RK approach is done without additional degrees of freedom associated with the enrichment functions, and it is formulated with the standard procedures in the RK shape function construction. This work focuses on identifying the accuracy and convergence properties of IM-RKPM for modeling the coupled electro-chemo-mechanical system. A linear patch test is formulated and numerically tested for the electro-chemo-mechanical coupled problem with a Butler–Volmer boundary condition representing the physical conditions in Li-ion battery microstructures. This is followed by verification of the optimal rates of convergence of IM-RKPM for solving the coupled problem with higher order solutions. The image-based modeling of Li-ion battery microstructures in the numerical examples demonstrates the applicability of the proposed method to realistic Li-ion battery materials modeling.
      PubDate: 2024-08-13
       
  • Development of agent-based mesh generator for flow analysis using deep
           reinforcement learning

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      Abstract: Abstract Computational fluid dynamics (CFD) has widespread application in research and industry. The quality of the mesh, particularly in the boundary layer, significantly influences the CFD accuracy. Despite its importance, the mesh generation process remains manual and time intensive, with the introduction of potential errors and inconsistencies. The limitations of traditional methods have prompted the recent exploration of deep reinforcement learning (DRL) for mesh generation. Although some studies have demonstrated the applicability of DRL in mesh generation, they have limitations in utilizing existing tools, thereby falling short of fully leveraging the potential of DRL. This study proposes a new boundary mesh generation method using DRL, namely an agent-based mesh generator. The nodes on the surface act as agents and optimize the paths into space to create high-quality meshes. Mesh generation is naturally suited to DRL owing to its computational nature and deterministic execution. However, challenges also arise, including training numerous agents simultaneously and managing their interdependencies in a vast state space. In this study, these challenges are addressed along with an investigation of the optimal learning conditions after formulating grid generation as a DRL task: defining states, agents, actions, and rewards. The derived optimal conditions are applied to generate two dimensional airfoil grids to validate the feasibility of the proposed approach.
      PubDate: 2024-08-11
       
  • Spectrum analysis of $$C^0$$ , $$C^1$$ , and $$G^1$$ constructions for
           extraordinary points

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      Abstract: Abstract G-splines are smooth spline surface representations that support control nets with arbitrary unstructured quadrilateral layout. Supporting any distribution of extraordinary points (EPs) is necessary to satisfactorily meet the demands of real-world engineering applications. G-splines impose \(G^1\) constraints across the edges emanating from the EPs, which leads to discretizations with global \(C^1\) continuity in physical space when used in isogeometric analysis (IGA). In this work, we perform spectrum analyses of G-splines for the first time. Our results suggest that G-splines do not have outliers near the boundary when uniform elements and control nets are used. When EPs are considered, G-splines result in significantly higher spectral accuracy than the D-patch framework. In addition, we develop G-spline discretizations that use bi-quartic elements around EPs instead of bi-quintic elements around EPs as it was the case in our preceding work. All the other elements are bi-cubic. Our evaluations of surface quality, convergence studies of linear elliptic boundary-value problems, and spectral analyses suggest that using bi-quartic elements around EPs is preferable for IGA since they result in similar performance as using bi-quintic elements around EPs while being more computationally efficient.
      PubDate: 2024-08-09
       
  • Phase-field modeling of fracture in viscoelastic–viscoplastic thermoset
           nanocomposites under cyclic and monolithic loading

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      Abstract: Abstract In this study, a finite deformation phase-field formulation is developed to investigate the effect of hygrothermal conditions on the viscoelastic–viscoplastic fracture behavior of epoxy nanocomposites under cyclic and monolithic loading. The formulation incorporates a definition of the Helmholtz free energy, which considers the effect of nanoparticles, moisture content, and temperature. The free energy is additively decomposed into a deviatoric equilibrium, a deviatoric non-equilibrium, and a volumetric contribution. The proposed derivation offers a realistic modeling of damage and viscoplasticity mechanisms in the nanocomposites by coupling the phase-field damage model and a viscoelastic–viscoplastic model. Numerical simulations are conducted to study the cyclic force–displacement response of both dry and saturated boehmite nanoparticle (BNP)/epoxy samples, considering BNP contents and temperature. Comparing numerical results with experimental data shows good agreement at various BNP contents. In addition, the predictive capability of the phase-field model is evaluated through simulations of notched nanocomposite plates subjected to monolithic tensile and shear loading.
      PubDate: 2024-08-08
       
  • A very fast high-order flux reconstruction for Finite Volume schemes for
           Computational Aeroacoustics

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      Abstract: Abstract Given the small wavelengths and wide range of frequencies of the acoustic waves involved in Aeroacoustics problems, the use of very accurate, low-dissipative numerical schemes is the only valid option to accurately capture these phenomena. However, as the order of the scheme increases, the computational time also increases. In this work, we propose a new high-order flux reconstruction in the framework of finite volume (FV) schemes for linear problems. In particular, it is applied to solve the Linearized Euler Equations, which are widely used in the field of Computational Aeroacoustics. This new reconstruction is very efficient and well suited in the context of very high-order FV schemes, where the computation of high-order flux integrals are needed at cell edges/faces. Different benchmark test cases are carried out to analyze the accuracy and the efficiency of the proposed flux reconstruction. The proposed methodology preserves the accuracy while the computational time relatively reduces drastically as the order increases.
      PubDate: 2024-08-06
       
  • Deep NURBS—admissible physics-informed neural networks

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      Abstract: Abstract In this study, we propose a new numerical scheme for physics-informed neural networks (PINNs) that enables precise and inexpensive solutions for partial differential equations (PDEs) in case of arbitrary geometries while strongly enforcing Dirichlet boundary conditions. The proposed approach combines admissible NURBS parametrizations (admissible in the calculus of variations sense, that is, satisfying the boundary conditions) required to define the physical domain and the Dirichlet boundary conditions with a PINN solver. Therefore, the boundary conditions are automatically satisfied in this novel Deep NURBS framework. Furthermore, our sampling is carried out in the parametric space and mapped to the physical domain. This parametric sampling works as an importance sampling scheme since there is a concentration of points in regions where the geometry is more complex. We verified our new approach using two-dimensional elliptic PDEs when considering arbitrary geometries, including non-Lipschitz domains. Compared to the classical PINN solver, the Deep NURBS estimator has a remarkably high accuracy for all the studied problems. Moreover, a desirable accuracy was obtained for most of the studied PDEs using only one hidden layer of neural networks. This novel approach is considered to pave the way for more effective solutions for high-dimensional problems by allowing for a more realistic physics-informed statistical learning framework to solve PDEs.
      PubDate: 2024-08-05
       
  • A novel analytical model of particle size distributions in granular
           materials

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      Abstract: Abstract The analysis of particle size distributions is important to better understand the relation between the microstructure and the heterogenous physical behavior of granular materials, including soils, sands, and concrete. This paper presents a novel analytical model, entitled piecewise linear sieve curve, to accurately reproduce the complicated and wide-ranging particle size distribution of granular materials. The model assumes that the passing percentage varies linearly with aggregate size between two adjacent sieves. The probability density function and cumulative distribution function of the piecewise linear sieve curve can be determined directly once the experimental particle gradation is known. Several types of concrete with different mix designs were taken as numerical examples, and the particle modeling based on piecewise linear sieve curve and the classical Fuller curve were compared. The results show that the piecewise linear sieve curve provides a much better representation of different aggregate particle size distributions than the Fuller curve, and the proposed model achieves the goal to reproduce the experimental aggregate gradation in an efficient and accurate way.
      PubDate: 2024-08-05
       
  • A physics-informed parametrization and its impact on 2D IGABEM analysis

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      Abstract: Abstract In this work, we study the effect of the geometry representation in the context of the IsoGeometric-Analysis-based Boundary Element Method (IGABEM) and we propose an algorithm for the construction of a physics-informed geometric representation which leads to approximation results of high accuracy that are comparable to known adaptive refinement schemes. As a model problem, we use a previously studied 2D potential flow problem around a cylinder; see Politis et al. (Proceedings of SIAM/ACM joint conference on geometric and physical modeling, California, pp 349–354, 2009. https://doi.org/10.1145/1629255.1629302L). This study involves a systematic examination of a series of transformations and reparametrizations and their effect on the achieved accuracy and convergence rate of the numerical solution to the problem at hand. Subsequently, a new parametrization is proposed based on a coarse-level approximation of the field-quantity solution, coupling in this way the geometry representation to the physics of the problem. Finally, the performance of our approach is compared against an exact-solution-driven adaptive refinement scheme and a posteriori error estimates for adaptive IGABEM methods. The proposed methodology delivers results of similar quality to the adaptive approaches, but without the computational cost of error estimates evaluation at each refinement step.
      PubDate: 2024-08-03
       
  • Accurate numerical simulations of capillary underfill process for
           flip-chip packages

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      Abstract: Abstract In the capillary underfill packaging process, resin with specific characteristics such as low viscosity, high flowability, fast curing, and high reliability is utilized to fill the gaps between the substrate and the die. This underfill resin serves to reinforce the connections between metal bumps and the substrate, thereby extending the lifespan and enhancing the reliability of FCBGA (Flip-Chip Ball Grid Array) packages. Despite the availability of flow simulation tools, the development of the underfill process remains a significant challenge for engineers due to the multitude of control parameters involved. The objective of this study is to identify the key factors influencing the accuracy of underfill flow simulations and explore potential solutions to these challenges. In this study, it is found that necessary ingredients for accurate underfill simulation need to include the following items: 1. Good flow simulation software 2. Accurately measured material properties 3. Good and fine mesh 4. Right amount of dispensed resin 5. Right timing for resin dispensing. The accuracy of the simulation is particularly affected by factors such as overflowing, resin climbing, non-uniform flow, and air trapping, which are influenced by the amount and timing of resin dispensing. By addressing these factors, this study demonstrates that accurate underfill simulation can be achieved, providing valuable insights into microscale flip-chip underfill physics. This research lays the groundwork for the development of validated models applicable to next-generation high-density flip-chip products.
      PubDate: 2024-08-03
       
  • Buckling analysis of functionally graded sandwich thin plates using a
           meshfree Hermite Radial Point Interpolation Method

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      Abstract: Abstract This paper introduces an innovative mesh-free computational approach for simulating problems with geometric nonlinearity, focusing on the buckling analysis of thin plates. Addressing significant deformations, the study formulates governing partial differential equations based on Kirchhoff’s plate theory and discretizes them using the Galerkin method. To tackle the complexities of this problem, which demands higher-order continuity in shape functions and accommodates both Dirichlet and Neumann boundary conditions, the research extends the Hermite-type point interpolation method (HPIM). Despite HPIM’s effectiveness, occasional singularities in the moment matrix require enhancement. This work proposes an improved Hermite-type point interpolation method augmented by radial basis functions (Hermite-RPIM) to ensure a well-conditioned moment matrix. The efficacy of the proposed method is validated through detailed numerical examples, including buckling and post-buckling analysis of sandwich functionally graded material (FGM) plates under various loadings, boundary conditions, and material types. These examples highlight the robustness, reliability, and computational efficiency of the enhanced Hermite-RPIM, establishing its potential as a valuable tool for analyzing geometrically nonlinear problems, especially in thin plate buckling analysis.
      PubDate: 2024-08-02
       
  • A finite element-based physics-informed operator learning framework for
           spatiotemporal partial differential equations on arbitrary domains

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      Abstract: Abstract We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (PDEs). The Galerkin discretized weak formulation is employed to incorporate physics into the loss function, termed finite operator learning (FOL), along with the implicit Euler time integration scheme for temporal discretization. A transient thermal conduction problem is considered to benchmark the performance, where FOL takes a temperature field at the current time step as input and predicts a temperature field at the next time step. Upon training, the network successfully predicts the temperature evolution over time for any initial temperature field at high accuracy compared to the solution by the finite element method (FEM) even with a heterogeneous thermal conductivity and arbitrary geometry. The advantages of FOL can be summarized as follows: First, the training is performed in an unsupervised manner, avoiding the need for large data prepared from costly simulations or experiments. Instead, random temperature patterns generated by the Gaussian random process and the Fourier series, combined with constant temperature fields, are used as training data to cover possible temperature cases. Additionally, shape functions and backward difference approximation are exploited for the domain discretization, resulting in a purely algebraic equation. This enhances training efficiency, as one avoids time-consuming automatic differentiation in optimizing weights and biases while accepting possible discretization errors. Finally, thanks to the interpolation power of FEM, any arbitrary geometry with heterogeneous microstructure can be handled with FOL, which is crucial to addressing various engineering application scenarios.
      PubDate: 2024-08-02
       
  • Image-to-mesh conversion method for multi-tissue medical image computing
           simulations

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      Abstract: Abstract Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh’s fidelity. The deformation scheme builds upon the ITK open-source library and is based on the concept of energy minimization, relying on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of extracted feature points needed for the registration and, thus, adjusts the trade-off between the achieved mesh fidelity and the deformation speed. We compare CBC3D with four widely used and state-of-the-art homegrown image-to-mesh conversion methods from industry and academia. Results indicate that the CBC3D meshes: (1) achieve high fidelity, (2) keep the element count reasonably low, and (3) exhibit good element quality.
      PubDate: 2024-08-01
       
  • Multiple scattering simulation via physics-informed neural networks

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      Abstract: Abstract This work presents a physics-driven machine learning framework for the simulation of acoustic scattering problems. The proposed framework relies on a physics-informed neural network (PINN) architecture that leverages prior knowledge based on the physics of the scattering problem as well as a tailored network structure that embodies the concept of the superposition principle of linear wave interaction. The framework can also simulate the scattered field due to rigid scatterers having arbitrary shape as well as high-frequency problems. Unlike conventional data-driven neural networks, the PINN is trained by directly enforcing the governing equations describing the underlying physics, hence without relying on any labeled training dataset. Remarkably, the network model has significantly lower discretization dependence and offers simulation capabilities akin to parallel computation. This feature is particularly beneficial to address computational challenges typically associated with conventional mesh-dependent simulation methods. The performance of the network is investigated via a comprehensive numerical study that explores different application scenarios based on acoustic scattering.
      PubDate: 2024-07-30
       
  • A stable meshfree method for simulations of munition penetration into
           earth

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      Abstract: Abstract Meshfree methods, such as the Reproducing Kernel Particle Method, have been proven advantageous in modeling excessive deformation problems involving material separation, fracture, impact, etc. However, the domain integration in RKPM remains challenging due to instability and sub-optimal convergence for high strain rate events. Although some novel developments alleviate the above issue, they are either computationally expensive or require evaluating the contour integral, which is not straightforward to obtain in contact and material separation problems using meshfree discretization. This work develops a simple and stable integration method based on the extension of modified Simpson’s rule. The method is free from conforming subdomains and can straightforwardly be applied to the meshfree formulation with updated configuration. To model penetration into the earth, a standard viscous boundary is introduced to address the issue of reflecting waves from the truncated computational domain for the ground target. The numerical results are validated with experimental data for various geo-materials and experimental setups.
      PubDate: 2024-07-29
       
 
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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 293)
Statistics in Medicine     Hybrid Journal   (Followers: 156)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 78, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Biometrics     Hybrid Journal   (Followers: 51)
Sociological Methods & Research     Hybrid Journal   (Followers: 50)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 42, SJR: 3.664, CiteScore: 2)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 39)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 27)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 25)
Journal of Applied Statistics     Hybrid Journal   (Followers: 22)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Computational Statistics     Hybrid Journal   (Followers: 17)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Risk Management     Hybrid Journal   (Followers: 16)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Stata Journal     Full-text available via subscription   (Followers: 10)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Handbook of Statistics     Full-text available via subscription   (Followers: 9)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Current Research in Biostatistics     Open Access   (Followers: 9)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Argumentation et analyse du discours     Open Access   (Followers: 8)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Significance     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
TEST     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  

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Tel: +00 44 (0)131 4513762
 


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