
样式: 排序: IF: - GO 导出 标记为已读
-
AK-UL: An active learning kriging method based on uniform sampling and local refinement for efficient reliability analysis with small failure probability Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-20 Yong Pang, Xiwang He, Pengwei Liang, Xueguan Song, Ziyun Kan
This paper introduces a novel active learning kriging-based reliability analysis method that uses uniform sampling and local refinement, termed AK-UL, with a focus on problems involving small failure probabilities. Traditional methods, such as AK-MCS, struggle to identify failure regions efficiently because of the irregular distribution of candidate points and the high computational cost of generating
-
Transitional active learning of small probabilities Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-20 Pengfei Wei
Efficient estimation of small failure probability subjected to multiple failure domains is one of the central and challenging issues in structural reliability analysis and other rare event analysis tasks, especially in case where the computational resource is quite limited but high accuracy is required. A new active learning scheme, named as Transitional Bayesian Quadrature (TBQ), is proposed to fill
-
FFV-PINN: A fast physics-informed neural network with simplified finite volume discretization and residual correction Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-20 Chang Wei, Yuchen Fan, Jian Cheng Wong, Chin Chun Ooi, Heyang Wang, Pao-Hsiung Chiu
With the growing application of deep learning techniques in computational physics, physics-informed neural networks (PINNs) have emerged as a major research focus. However, today’s PINNs encounter several limitations. Firstly, during the construction of the loss function using automatic differentiation, PINNs often neglect information from neighboring points, which hinders their ability to enforce
-
Homogenization in hyperelasticity using Empirically Corrected Cluster Cubature (E3C) hyper-reduction Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-20 Stephan Wulfinghoff
Computational homogenization methods open the possibility to simulate engineering structures on two scales simultaneously and to accurately describe complex macroscopic material behavior. Their intrinsically high computational cost can be alleviated through model order reduction methods in combination with hyper-reduction. The recently proposed E3C hyper-reduction method is applied to plane-strain
-
Development and validation of an offline multiscale topology optimization framework using interpolated constraint functions Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-20 Brent Bielefeldt, Richard Beblo, Kevin Lawson, Edward Meixner, Robert Lowe
Multiscale structural design is an emerging field within the aerospace community driven by the need for innovative structural concepts capable of fulfilling ever-expanding performance requirements. However, exploring novel material systems or architectures at the preliminary design stage can be inefficient due to potential changes in objectives, boundary conditions, and constraints. Such changes often
-
An adaptive phase-field model integrated with multi-patch isogeometric analysis and adaptive cycle jump scheme for thermo-electro-mechanical fatigue fracture in flexoelectric solids Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-19 Haozhi Li, Tiantang Yu, Zhaowei Liu, Jia-Nan He, Leilei Chen
Predicting the thermal fatigue life of flexoelectric components is of great engineering significance. In this study, an effective adaptive phase-field model combined with the cycle jump scheme is proposed to simulate thermo-electro-mechanical fatigue fracture in flexoelectric solids. To provide C1 continuity due to the presence of strain gradients, the phase-field model is implemented in the multi-patch
-
Manufacturability-aware topology and toolpath co-design for continuous fiber-reinforced composites additive manufacturing Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-18 Huilin Ren, Ziwen Chen, Xiaoxiao Shen, Yi Xiong
Design for continuous fiber-reinforced polymer additive manufacturing (CFRP-AM) has evolved from sequential approaches to concurrent design methods, enabling the simultaneous optimization of structural topology and fiber toolpaths. However, existing studies fails to consider the manufacturing constraints inherent to CFRP-AM, such as toolpath width consistency and fiber continuity, which are critical
-
T-splines-based panel method for aerodynamic topology optimization of engineering shell structures using isogeometric analysis Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-18 Xiao Zhang, Liang Gao, Mi Xiao, Jie Gao
Engineering shell structures have been extensively used in aerospace, automotive and other fields, whose aerodynamic performance is significant in structural design. In the current work, the primary intention is to propose an aerodynamic topology optimization design framework for arbitrary engineering shell structures using the T-splines-based panel method and isogeometric analysis. Firstly, the T-splines-based
-
A regular-vine copula-based evidence theory model for structural reliability analysis involving multidimensional parameter correlation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-18 Dequan Zhang, Xinjue Xie, Zhijie Hao, Weipeng Liu, Xu Han, Qing Li
The structural reliability analysis subject to epistemic uncertainty and multidimensional correlations among input variables signifies a crucial and demanding task. To address this challenge, a new evidence theory model capable of quantifying complex multidimensional correlations is proposed in this study; and further, an efficient reliability analysis method is developed. To start with, the multidimensional
-
Two-scale concurrent topology optimization of multiple lattice materials with non-uniform thickness interfaces Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-18 Chao Li, Qihan Wang, Minghui Zhang, Wei Gao, Zhen Luo
Structures composed of multiple lattice materials have exceptional mechanical properties due to their rationally designed macrostructures and microstructures, which have enabled diverse engineering applications. However, existing topology optimization approaches for such structures often face challenges in achieving generality and flexibility, particularly in designing the thickness of interfacial
-
Adaptive deep physics-informed neural network with dual-nested activation for solving complex partial differential equations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-17 Tianhao Wang, Guirong Liu, Eric Li, Xu Xu
Physics-informed neural networks (PINNs) hold promise for solving partial differential equations (PDEs), but they often face challenges in achieving high accuracy, especially in complex, real-world scenarios. This paper presents an adaptive deep PINN (ad-PINN) framework designed to enhance the efficiency of both activation and loss functions. The proposed ad-PINN introduces two main innovations: (1)
-
A coupled mathematical and numerical model for protein spreading and tissue atrophy applied to Alzheimer’s disease Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-17 Valentina Pederzoli, Mattia Corti, Davide Riccobelli, Paola F. Antonietti
The aim of this paper is to introduce, analyze and test in practice a new mathematical model describing the interplay between biological tissue atrophy driven by the diffusion of a biological agent, with applications to neurodegenerative disorders. This study introduces a novel mathematical and computational model comprising a Fisher–Kolmogorov equation for species diffusion coupled with an elasticity
-
Robust trimmed multipatch IGA with singular maps Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-16 Tobias Jonsson, Mats G. Larson, Karl Larsson
We consider elliptic problems in multipatch isogeometric analysis (IGA) where the patch parameterizations may be singular. Specifically, we address cases where certain dimensions of the parametric geometry diminish as the singularity is approached — for example, a curve collapsing into a point (in 2D), or a surface collapsing into a point or a curve (in 3D). To deal with this issue, we develop a robust
-
Multiscale topology optimization of architected fiber reinforced composites considering manufacturability Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-16 Arijit Pradhan, Narasimha Boddeti
Advances in additive manufacturing and tow-steered processes are now enabling the fabrication of fiber-reinforced composites (FRCs) with architected microstructures, via complex curvilinear fiber paths/layouts, for desired structural response at the macroscale. Engineers can leverage these advances, via multiscale topology optimization (MTO), to design structures with optimized macro and microstructures
-
On shells of revolution with random profiles Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-14 Stefano Giani, Harri Hakula, Duc Khuat
Thin structures and shells in particular are well-known to be highly sensitive to manufacturing imperfections such as perturbations on the profile of a shell of revolution. The main result of this study is that one cannot expect to apply standard models for perturbations, such as Karhunen–Loève expansions, without careful consideration on how the applied model depends on the regularity of the random
-
Surrogate models of stress for triply periodic minimal surface lattices Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-13 Sy Nguyen-Van, Guha Manogharan, Lan-Hsuan Huang, Julián A. Norato
This work formulates compact and efficient surrogates of the stress field for sheet-network, triply periodic minimal surface (TPMS) lattices, which have been gaining popularity due to advancements in additive manufacturing methods. The proposed surrogates can be employed to determine, for example, the largest von Mises or principal stresses in a TPMS lattice as a function of the wall thickness, base
-
Quasi-static loading of granular media as a linear complementarity problem Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-13 Matthew R. Kuhn
The discrete quasi-static response of rate-independent dissipative granular media is addressed. Granular systems are conventionally simulated with methods that are intrinsically dynamic, such as the discrete element (DEM) and discontinuous deformation (DDA) methods, with the particles’ accelerations and damping being essential aspects. In contrast, quasi-static methods derive from the static stiffness
-
Efficient Bayesian updating with single-loop Kriging model for time-dependent model calibration Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-12 Zhao-Hui Lu, Wan-Ting Pei, Zhao Zhao, Zengzhi Qian
Bayesian updating, as a useful tool for system identification and model calibration, has gained signification traction in recent years. However, for time-dependent models, the number of observations will increase rapidly with the increase of the number of time nodes, resulting in Bayesian updating problems facing serious challenges. To settle this issue, this paper proposes an efficient Bayesian updating
-
Non-intrusive reduced-order modeling for dynamical systems with spatially localized features Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-12 Leonidas Gkimisis, Nicole Aretz, Marco Tezzele, Thomas Richter, Peter Benner, Karen E. Willcox
This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary
-
A chain stretch-based gradient-enhanced model for damage and fracture in elastomers Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-12 S. Mohammad Mousavi, Jason Mulderrig, Brandon Talamini, Nikolaos Bouklas
Similar to quasi-brittle materials, it has been recently shown that elastomers can exhibit a macroscopically diffuse damage zone that accompanies the fracture process. In this study, we introduce a stretch-based gradient-enhanced damage (GED) model that allows the fracture to localize and also captures the development of a physically diffuse damage zone. This capability contrasts with the paradigm
-
A posteriori algebraic error estimates and nonoverlapping domain decomposition in mixed formulations: energy coarse grid balancing, local mass conservation on each step, and line search Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-12 Manuela Bastidas Olivares, Akram Beni Hamad, Martin Vohralík, Ivan Yotov
We consider iterative algebraic solvers for saddle-point mixed finite element discretizations of the model Darcy flow problem. We propose a posteriori error estimators of the algebraic error as well as a nonoverlapping domain decomposition algorithm. The estimators control the algebraic error from above and from below in a guaranteed and fully computable way. The distinctive feature of the domain decomposition
-
Isogeometric topology optimization of thin-walled structures with complex design domains Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-11 Ji Sheng, Xiaodong Wei
In this work, we present a novel isogeometric topology optimization (TO) method for shell structures that involve complex design domains. In particular, analysis-suitable unstructured T-splines (ASUTS) are used to represent complex design domains in a smooth and watertight manner. On top of such domains, minimum compliance is studied as the model problem, where the Kirchhoff–Love shell is used to compute
-
A resolution independent neural operator Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-11 Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis, Michael D. Shields
The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evaluation of the solution field at any location within the desired domain. However, it imposes a strict constraint on the input space, requiring all input
-
Stabilizing and solving unique continuation problems by parameterizing data and learning finite element solution operators Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-10 Erik Burman, Mats G. Larson, Karl Larsson, Carl Lundholm
We consider an inverse problem involving the reconstruction of the solution to a nonlinear partial differential equation (PDE) with unknown boundary conditions. Instead of direct boundary data, we are provided with a large dataset of boundary observations for typical solutions (collective data) and a bulk measurement of a specific realization. To leverage this collective data, we first compress the
-
Tensor-decomposition-based A Priori Surrogate (TAPS) modeling for ultra large-scale simulations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-09 Jiachen Guo, Gino Domel, Chanwook Park, Hantao Zhang, Ozgur Can Gumus, Ye Lu, Gregory J. Wagner, Dong Qian, Jian Cao, Thomas J.R. Hughes, Wing Kam Liu
A data-free predictive scientific AI model, termed Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significant speedup, memory savings, and storage gain. TAPS does not require any training data and can effectively obtain surrogate models for high-dimensional parametric problems with equivalently zetta-scale (1021) degrees
-
A hybrid phase-field model for dynamic fracture in fiber-reinforced composites considering interfacial debonding Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-06 Nhon Nguyen-Thanh, Weidong Li, Qi Zhang, Kun Zhou
In this work, we develop a hybrid phase-field modeling approach, enhanced by a higher-order nonlocal operator method (NOM) to simulate dynamic brittle fracture in fiber-reinforced composites. This approach captures dynamic fracture patterns in composite materials, including matrix cracking, interfacial debonding, and the interaction between these failure modes. A crack surface density function is applied
-
A manufacturable fiber placement method for continuously fitting optimized discrete fiber orientations based on B-spline equidistant curves Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-06 Chengxiang Han, Xiangkui Zhang, Ping Hu, Guojun Zheng, Xuefeng Zhu, Guozhe Shen
Equidistant fiber placement structures exhibit significant advantages in both mechanical performance and manufacturability. This paper addresses the challenge of forming continuous, manufacturable fiber paths from discrete fiber optimization results. Leveraging the ability of B-splines to represent complex shapes and offer local control, we propose a method that uses B-spline as the base curve for
-
A porosity-based mechanics model for studying crack evolution from ITZ to mortar matrix in concrete Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-06 Zesen Peng, Qing-xiang Xiong, Xiangming Zhou, Xuan Gao, Xin-Yu Zhao, Zhaozheng Meng, Qing-feng Liu
This study proposes a novel porosity-based mechanics model for investigating the crack evolution in concrete under uniaxial compression. This model accounts for the porosity gradient and heterogeneous mechanical properties within the concrete’s interfacial transition zone (ITZ). Validation against experimental results from the literature and international standards demonstrates the model’s accuracy
-
Time-dependent reliability analysis by a single-loop Bayesian active learning method using Gaussian process regression Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-05 Chao Dang, Marcos A. Valdebenito, Matthias G.R. Faes
Time-dependent reliability analysis has proven to be an invaluable tool for assessing the safety levels of engineering structures subject to both randomness and time-varying factors. In this context, single-loop active learning Kriging methods have demonstrated a favorable trade-off between efficiency and accuracy. However, there remains significant potential for further improvement, particularly in
-
Enhancing dynamic modeling of porous media with compressible fluid: A THM material point method with improved fractional step formulation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-04 Jidu Yu, Weijian Liang, Jidong Zhao
Modeling dynamic behavior and large deformation in porous media, encompassing coupled fluid flow, solid deformation, and heat transfer, remains a critical challenge in geomechanics. While the two-phase material point method (MPM) combined with the semi-implicit fractional step method (FSM) has demonstrated efficacy for saturated porous media under large deformation, traditional FSM is constrained to
-
Convolutional neural network-based reduced-order modeling for parametric nonlocal PDEs Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-04 Yumeng Wang, Shiping Zhou, Yanzhi Zhang
In this paper, we propose a convolutional neural network (CNN) based reduced-order modeling (ROM) to solve parametric nonlocal partial differential equations (PDEs). Our method consists of two main components: dimensional reduction with convolutional autoencoder (CAE) and latent-space modeling with CNN or long short-term memory (LSTM) networks. Our neural network-based ROM bypasses the main challenges
-
Accurate 3D stress recovery in elastic laminated plates using 5-DOF and 7-DOF finite element plate models with warping Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-04 Domenico Magisano, Leonardo Leonetti, Giovanni Garcea
This paper presents an efficient and accurate methodology for computing displacement and stress fields in laminated thick plates using two-dimensional models. The approach begins with a novel one-dimensional finite element analysis across the thickness to derive transverse shear warping functions for a given layup. This preliminary analysis ensures accuracy for generic laminations, including asymmetric
-
-
A variational computational-based framework for unsteady incompressible flows Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-03 H. Sababha, A. Elmaradny, H. Taha, M. Daqaq
Advancements in computational fluid mechanics have largely relied on Newtonian frameworks, particularly through the direct simulation of Navier–Stokes equations. In this work, we propose an alternative computational framework that employs variational methods, specifically by leveraging the principle of minimum pressure gradient, which turns the fluid mechanics problem into a minimization problem whose
-
Integrated design of structures and supports using a hybrid explicit–implicit topology optimization method Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-06-03 Rixin Wang, Benliang Zhu, Xianmin Zhang, Fumihito Arai
In engineering structures and mechanical systems, the arrangement of supports or Dirichlet boundary conditions determines the constraint conditions and the distribution of degrees of freedom, thereby exerting a critical influence on performances. This paper proposes a novel integrated design method for structures and supports. First, a hybrid explicit–implicit topology description framework is developed
-
Multi-material topology optimization of vibro-acoustic structures with acoustic, poroelastic and elastic media under mass constraint Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-31 Jie Hu, Jiachun Li, Xing Chen, Jiao Xu, Xiaodong Huang
Single-material topology optimization designs struggle to achieve the free selection of multiple materials in vibro-acoustic structures while adhering to budgetary and spatial constraints to obtain optimal objective performance. To address this challenge, this paper proposes a novel topology optimization approach tailored for multi-material vibro-acoustic structures based on the multi-material floating
-
Variational sequential optimal experimental design using reinforcement learning Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-31 Wanggang Shen, Jiayuan Dong, Xun Huan
We present variational sequential optimal experimental design (vsOED), a novel method for optimally designing a finite sequence of experiments within a Bayesian framework with information-theoretic criteria. vsOED employs a one-point reward formulation with variational posterior approximations, providing a provable lower bound to the expected information gain. Numerical methods are developed following
-
A four-field mixed formulation for incompressible finite elasticity Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-31 Guosheng Fu, Michael Neunteufel, Joachim Schöberl, Adam Zdunek
In this work, we generalize the mass-conserving mixed stress (MCS) finite element method for Stokes equations (Gopalakrishnan et al., 2019), involving normal velocity and tangential-normal stress continuous fields, to incompressible finite elasticity. By means of the three-field Hu–Washizu principle, introducing the displacement gradient and 1st Piola–Kirchhoff stress tensor as additional fields, we
-
Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs) Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-29 Prakash Thakolkaran, Yaqi Guo, Shivam Saini, Mathias Peirlinck, Benjamin Alheit, Siddhant Kumar
Traditional constitutive models rely on hand-crafted parametric forms with limited expressivity and generalizability, while neural network-based models can capture complex material behavior but often lack interpretability. To balance these trade-offs, we present monotonic Input-Convex Kolmogorov-Arnold Networks (ICKANs) for learning polyconvex hyperelastic constitutive laws. ICKANs leverage the Kolmogorov-Arnold
-
A nodal ghost method based on variational formulation and regular square grid for elliptic problems on arbitrary domains in two space dimensions Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-29 Clarissa Astuto, Daniele Boffi, Giovanni Russo, Umberto Zerbinati
This paper focuses on the numerical solution of elliptic partial differential equations (PDEs) with Dirichlet and mixed boundary conditions, specifically addressing the challenges arising from irregular domains. Both finite element method (FEM) and finite difference method (FDM), face difficulties in dealing with arbitrary domains. The paper introduces a novel nodal symmetric ghost method based on
-
Integrating discrete-variable anisotropic topology optimization with lamination parameter interpolation-based stiffness tailoring Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-26 Kai Sun, Gengdong Cheng, Gokhan Serhat
This paper introduces a novel computational design framework for topology and fiber path optimization of variable stiffness laminated composites. Based on the finite element discretization of the design domain, the topology and material stiffness are represented using elemental densities and lamination parameters (LPs), respectively. The density distribution is optimized via the discrete-variable topology
-
A partitioned Lagrangian finite element approach for the simulation of viscoelastic and elasto-viscoplastic free-surface flows Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-26 Giacomo Rizzieri, Liberato Ferrara, Massimiliano Cremonesi
Many materials, such as clays, fresh concrete, and biological fluids, exhibit elasto-viscoplastic (EVP) behaviour, transitioning between solid and fluid states under varying stress conditions. Among EVP models, Saramito’s constitutive law stands out for its thermodynamic consistency, smooth solid-to-fluid transition, and ability to accurately represent diverse materials with only four easily determinable
-
FFT-based Galerkin and level-set methods for the homogenized evolution of domain nuclei in ferroelectrics Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-25 Hsu-Cheng Cheng, Lu Trong Khiem Nguyen, Dennis M. Kochmann
We introduce an FFT-based Galerkin homogenization scheme, which, in combination with the level-set method, allows us to study the evolution of ferroelectric domain nuclei in the experimentally relevant stress-driven setting. Our proposed framework includes an accelerated FFT-solver for solving the electromechanically coupled balance laws and a unified regularized driving force formulation for the level-set
-
Time series clustering adaptive enhanced method for time-dependent reliability analysis and design optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-24 Dequan Zhang, Ying Zhao, Meide Yang, Chao Jiang, Xu Han, Qing Li
Adaptive Kriging model has gained growing attention for its effectiveness in reducing the computational costs in time-dependent reliability analysis (TRA). However, the existing methods struggle to identify critical sample regions, leverage parallel computational resources, and assess the value for sample trajectories, thus restricting improvement in accuracy and efficiency. To address the challenges
-
Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-24 Jice Zeng, Yuanzhe Wang, Alexandre M. Tartakovsky, David A. Barajas-Solano
We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates
-
Extending the Lattice Boltzmann Method to non-linear elastodynamics Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-23 Henning Müller, Erik Faust, Alexander Schlüter, Ralf Müller
This work outlines a Lattice Boltzmann Method (LBM) for geometrically and constitutively non-linear solid mechanics to simulate large deformations under dynamic loading conditions. The method utilises the moment chain approach, where the non-linear constitutive law is incorporated via a forcing term. Stress and deformation measures are expressed in the reference configuration. Finite difference schemes
-
Accurate shakedown analysis of 2D problems based on stabilization-free hybrid virtual elements Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-23 F.S. Liguori, A. Madeo, S. Marfia, E. Sacco, G. Garcea
Shakedown analyses require a precise evaluation of pointwise elastic stresses and, at the same time, an accurate representation of the elastoplastic solution for capturing the ratcheting mechanisms effectively. However, existing discretization methods often face a trade-off: techniques that optimize plasticity performance may compromise elastic accuracy, and vice versa. The Hybrid Virtual Element Method
-
Parallel constrained Bayesian optimization via batched Thompson sampling with enhanced active learning process for reliability-based design optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-22 Thu Van Huynh, Sawekchai Tangaramvong, Wei Gao
This paper proposes an effective and robust decoupled approach for addressing reliability-based design optimization (RBDO) problems. The method iteratively performs a parallel constrained Bayesian optimization (PCBO) with deterministic parameters based on the most probable point (MPP) underpinning limit-state functions (LSFs) sequentially updated through an enhanced active learning-based reliability
-
Reducing parameter tuning in topology optimization of flow problems using a Darcy and Forchheimer penalization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-22 M.J.B. Theulings, L. Noël, M. Langelaar, R. Maas
In density-based topology optimization of flow problems, flow in the solid domain is generally inhibited using a penalization approach. Setting an appropriate maximum magnitude for the penalization traditionally requires manual tuning to find an acceptable compromise between flow solution accuracy and design convergence. In this work, three penalization approaches are examined, the Darcy (D), the Darcy
-
Double-scale time-dependent reliable topology optimization based on the first-passage failure and interval process theories Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Xingyu Zhao, Lei Wang
In this paper, a time-dependent reliability-based double-scale topology optimization (TO) framework considering manufacturability is proposed. The proposed TO model focuses on the structural transient dynamic performance subjected to general dynamic loads. A time-dependent interval non-probabilistic reliability theory based on the idea of first-passage is introduced into the double-scale TO model to
-
Quantum computer formulation of the FKP-operator eigenvalue problem for probabilistic learning on manifolds Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Christian Soize, Loïc Joubert-Doriol, Artur F. Izmaylov
We present a quantum computing formulation to address a challenging problem in the development of probabilistic learning on manifolds (PLoM). It involves solving the spectral problem of the high-dimensional Fokker–Planck (FKP) operator, which remains beyond the reach of classical computing. Our ultimate goal is to develop an efficient approach for practical computations on quantum computers. For now
-
Deep mechanics prior - for the multiscale finite element method Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Senlin Huo, Yong Zhao, Bingxiao Du, Zeyu Zhang, Yaqi Cao, Yiyu Du
The Multiscale Finite Element Method (MsFEM) decomposes the problem of solving partial differential equations with multiscale characteristics into two subproblems at two discrete resolution levels, i.e., the macroscopic one on a coarse mesh and the microscopic one on a fine mesh. The microscopic subproblems are used for constructing the Equivalent Stiffness Matrices (ESMs) of the coarse elements, and
-
Mesh-based super-resolution of fluid flows with multiscale graph neural networks Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Shivam Barwey, Pinaki Pal, Saumil Patel, Riccardo Balin, Bethany Lusch, Venkatram Vishwanath, Romit Maulik, Ramesh Balakrishnan
A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations
-
Adaptive phase-field modeling for electromechanical fracture in flexoelectric materials using multi-patch isogeometric analysis Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Haozhi Li, Tiantang Yu, Zhaowei Liu, Jiaping Sun, Leilei Chen
The fracture of flexoelectric materials involves strain gradients, which pose challenges for theoretical and numerical analysis. The phase-field model (PFM) is highly effective for simulating crack propagation. However, PFM within the finite element method (FEM) framework faces certain challenges in simulating the fracture behavior of flexoelectric materials since the conventional FEM can only provide
-
A unit cell based multilevel substructuring method for fast vibration response calculations of finite metamaterial structures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Fei Qu, Lucas Van Belle, Wim Desmet, Elke Deckers
Locally resonant metamaterial structures have gained significant attention across multiple engineering disciplines due to their ability to exhibit vibration stop bands not found in regular materials. These structures are composed of an assembly of unit cells, which are often discretized into large finite element models due to their sub-wavelength nature and intricate design. Moreover, due to the contribution
-
Adaptive phase-field cohesive-zone model for simulation of mixed-mode interfacial and bulk fracture in heterogeneous materials with directional energy decomposition Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Pei-Liang Bian, Qinghui Liu, Heng Zhang, Hai Qing, Siegfried Schmauder, Tiantang Yu
Interfacial debonding, a critical failure mechanism in heterogeneous materials, is often characterized by mixed-mode fracture. This study develops a numerical framework to simulate bulk and interfacial fractures in composite materials. A phase-field cohesive zone model, incorporating a directional energy decomposition scheme and a modified toughness method, is employed to capture complex fracture behaviors
-
Learning physics-consistent material behavior from dynamic displacements Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Zhichao Han, Mohit Pundir, Olga Fink, David S. Kammer
Accurately modeling the mechanical behavior of materials is crucial for numerous engineering applications. The quality of these models depends directly on the accuracy of the constitutive law that defines the stress–strain relation. However, discovering these constitutive material laws remains a significant challenge, in particular when only material deformation data is available. To address this challenge
-
Point cloud neural operator for parametric PDEs on complex and variable geometries Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-21 Chenyu Zeng, Yanshu Zhang, Jiayi Zhou, Yuhan Wang, Zilin Wang, Yuhao Liu, Lei Wu, Daniel Zhengyu Huang
Surrogate models are critical for accelerating computationally expensive simulations in science and engineering, particularly for solving parametric partial differential equations (PDEs). Developing practical surrogate models poses significant challenges, particularly in handling geometrically complex and variable domains, which are often discretized as point clouds. In this work, we systematically
-
Back-Projection Diffusion: Solving the wideband inverse scattering problem with diffusion models Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-17 Borong Zhang, Martin Guerra, Qin Li, Leonardo Zepeda-Núñez
We present Wideband Back-Projection Diffusion, an end-to-end probabilistic framework for approximating the posterior distribution induced by the inverse scattering map from wideband scattering data. This framework produces highly accurate reconstructions, leveraging conditional diffusion models to draw samples, and also honors the symmetries of the underlying physics of wave-propagation. The procedure
-
Physics-informed non-intrusive reduced-order modeling of parameterized dynamical systems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-05-16 Himanshu Dave, Léo Cotteleer, Alessandro Parente
In this study, we present a new framework of physics-informed non-intrusive reduced-order modeling (ROM) of dynamical systems modeled by parametric, partial differential equations (PDEs). Given new time and parameter values of a PDE, the framework utilizes trained physics-informed ML models to quickly estimate high-fidelity solutions while simultaneously observing the constraints and dynamics of the