Computer Methods in Applied Mechanics and Engineering最新文献_第4页

Graph neural network surrogates for contacting deformable bodies with necessary and sufficient contact detection 基于图神经网络的可变形物体接触检测方法

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-26 DOI: 10.1016/j.cma.2025.118413

Vijay K. Dubey , Collin E. Haese , Osman Gültekin , David Dalton , Manuel K. Rausch , Jan Fuhg

{"title":"Graph neural network surrogates for contacting deformable bodies with necessary and sufficient contact detection","authors":"Vijay K. Dubey , Collin E. Haese , Osman Gültekin , David Dalton , Manuel K. Rausch , Jan Fuhg","doi":"10.1016/j.cma.2025.118413","DOIUrl":"10.1016/j.cma.2025.118413","url":null,"abstract":"<div><div>Surrogate models for the rapid inference of nonlinear boundary value problems in mechanics are helpful in a broad range of engineering applications. However, effective surrogate modeling of applications involving the contact of deformable bodies, especially in the context of varying geometries, is still an open issue. In particular, existing methods are confined to rigid body contact or, at best, contact between rigid and soft objects with well-defined contact planes. Furthermore, they employ contact or collision detection filters that serve as a rapid test but use only the necessary and not sufficient conditions for detection. In this work, we present a graph neural network architecture that utilizes continuous collision detection and, for the first time, incorporates sufficient conditions designed for contact between soft deformable bodies. We test its performance on two benchmarks, including a problem in soft tissue mechanics of predicting the closed state of a bioprosthetic aortic valve. We find a regularizing effect on adding additional contact terms to the loss function, leading to better generalization of the network. These benefits hold for simple contact at similar planes and element normal angles, and complex contact at differing planes and element normal angles. We also demonstrate that the framework can handle varying reference geometries. However, such benefits come with high computational costs during training, resulting in a trade-off that may not always be favorable. We quantify the training cost and the resulting inference speedups on various hardware architectures. Importantly, our graph neural network implementation results in up to a hundred- to thousand-fold speedup on GPU, and twenty- to two hundred-fold speedup on CPU for our benchmark problems at inference.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118413"},"PeriodicalIF":7.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variable stiffness boundary condition to determine effective toughness of heterogeneous materials 确定非均质材料有效韧性的变刚度边界条件

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-26 DOI: 10.1016/j.cma.2025.118414

Tengyuan Hao , Adrian Piel , Zubaer Hossain

{"title":"Variable stiffness boundary condition to determine effective toughness of heterogeneous materials","authors":"Tengyuan Hao , Adrian Piel , Zubaer Hossain","doi":"10.1016/j.cma.2025.118414","DOIUrl":"10.1016/j.cma.2025.118414","url":null,"abstract":"<div><div>We present a computational framework that combines a variable stiffness boundary condition (VSBC) with a phase-field model to evaluate the effective fracture toughness of heterogeneous materials. It is built on the so-called surfing boundary condition (SBC) that applies nonuniform displacement at the remote boundary to stabilize crack-propagation in a heterogeneous medium. Unlike SBC, VSBC is easily implementable in traditional universal testing machines and commercial software packages. The VSBC passively translates a simple, uniform remote displacement into a non-uniform load via an engineered stiffness gradient, enabling the stable, natural propagation of cracks along energetically favorable paths. The framework is validated on homogeneous materials, where the calculated <span><math><mi>J</mi></math></span>-integral precisely matches the prescribed fracture toughness. When applied to heterogeneous domains, the VSBC method successfully quantifies the increase in effective toughness due to stiffness and toughness contrasts and captures the critical transition from crack penetration to deflection. Experimental validation using 3D-printed samples confirms the model’s predictive capability. The VSBC framework provides a robust easily accessible tool for investigating fracture in complex materials and guide the design of advanced, fracture-resistant composites.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118414"},"PeriodicalIF":7.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum neural network-assisted topology optimization: Concept and implementation with parameterized quantum circuits 量子神经网络辅助拓扑优化：参数化量子电路的概念与实现

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-25 DOI: 10.1016/j.cma.2025.118411

Naruethep Sukulthanasorn , Kenjiro Terada

{"title":"Quantum neural network-assisted topology optimization: Concept and implementation with parameterized quantum circuits","authors":"Naruethep Sukulthanasorn , Kenjiro Terada","doi":"10.1016/j.cma.2025.118411","DOIUrl":"10.1016/j.cma.2025.118411","url":null,"abstract":"<div><div>This study proposes a quantum machine learning-assisted approach to accelerating density-based topology optimization using quantum neural network (QNN), a class of trainable models based on parameterized quantum circuits (PQCs). The proposed framework extracts key features from classical data, starting by encoding the corresponding finite element analysis results, i.e., strain energy, sensitivity, and design variables from early design iterations obtained using the standard optimizer. Then, the PQCs are fine-tuned using minimization of the binary cross-entropy loss function, enabling the model to learn the mapping between input features and optimal design. Specifically, the framework consists of two main stages. First, an offline training stage where the QNN is calibrated using precomputed iterative results from a relatively coarse mesh obtained through the standard optimizer, establishing pattern recognition between input features and the final design variables. The second is an online stage where the trained QNN model is integrated with the standard optimizer to accelerate the final design. Numerical results show that QNN requires only a small number of qubits, and once trained on coarse meshes with several different boundary conditions, can effectively integrate with standard optimizers to predict target designs across various configurations, including different resolutions, volume constraints, and loading conditions. Furthermore, the proposed QNN-assisted framework significantly reduces computational time compared to standard iterative approaches, laying the groundwork for solving large-scale problems with near-term quantum computers.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118411"},"PeriodicalIF":7.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse data assimilation for under-resolved large-eddy simulations 欠分辨率大涡模拟的稀疏数据同化

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-25 DOI: 10.1016/j.cma.2025.118421

Justin Plogmann, Oliver Brenner, Patrick Jenny

{"title":"Sparse data assimilation for under-resolved large-eddy simulations","authors":"Justin Plogmann, Oliver Brenner, Patrick Jenny","doi":"10.1016/j.cma.2025.118421","DOIUrl":"10.1016/j.cma.2025.118421","url":null,"abstract":"<div><div>The need for accurate and fast scale-resolving simulations of fluid flows, where turbulent dispersion is a crucial physical feature, is evident. Large-eddy simulations (LES) are computationally more affordable than direct numerical simulations, but their accuracy depends on sub-grid scale models and the quality of the computational mesh. In order to compensate related errors, a data assimilation approach for LES is devised in this work. The presented method is based on variational assimilation of sparse time-averaged velocity reference data. Working with the time-averaged LES momentum equation allows to employ a stationary discrete adjoint method. Therefore, a stationary corrective force in the unsteady LES momentum equation is iteratively updated within the gradient-based optimization framework in conjunction with the adjoint gradient. After data assimilation, corrected anisotropic Reynolds stresses are inferred from the stationary corrective force. Ultimately, this corrective force that acts on the mean velocity is replaced by a term that scales the velocity fluctuations through nudging of the corrected anisotropic Reynolds stresses. Efficacy of the proposed framework is demonstrated for turbulent flow over periodic hills and around a square cylinder. Coarse meshes are leveraged to further enhance the speed of the optimization procedure. Time- and spanwise-averaged velocity reference data from high-fidelity simulations is taken from the literature. Our results demonstrate that adjoint-based assimilation of averaged velocity enables the optimization of the mean flow, vortex shedding frequency (i. e., Strouhal number), and anisotropic Reynolds stresses. This highlights the superiority of scale-resolving simulations such as LES over simulations based on the (unsteady) Reynolds-averaged equations.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118421"},"PeriodicalIF":7.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intrepid MCMC: Metropolis-Hastings with exploration 无畏的MCMC：大都会黑斯廷斯与探索

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-25 DOI: 10.1016/j.cma.2025.118402

Promit Chakroborty, Michael D. Shields

{"title":"Intrepid MCMC: Metropolis-Hastings with exploration","authors":"Promit Chakroborty, Michael D. Shields","doi":"10.1016/j.cma.2025.118402","DOIUrl":"10.1016/j.cma.2025.118402","url":null,"abstract":"<div><div>In engineering examples, one often encounters the need to sample from unnormalized distributions with complex shapes that may also be implicitly defined through a physical or numerical simulation model, making it computationally expensive to evaluate the associated density function. For such cases, MCMC has proven to be an invaluable tool. Random-walk Metropolis Methods (also known as Metropolis-Hastings (MH)), in particular, are highly popular for their simplicity, flexibility, and ease of implementation. However, most MH algorithms suffer from significant limitations when attempting to sample from distributions with multiple modes (particularly disconnected ones). In this paper, we present Intrepid MCMC - a novel MH scheme that utilizes a simple coordinate transformation to significantly improve the mode-finding ability and convergence rate to the target distribution of random-walk Markov chains while retaining most of the simplicity of the vanilla MH paradigm. Through multiple examples, we showcase the improvement in the performance of Intrepid MCMC over vanilla MH for a wide variety of target distribution shapes. We also provide an analysis of the mixing behavior of the Intrepid Markov chain, as well as the efficiency of our algorithm for increasing dimensions. A thorough discussion is presented on the practical implementation of the Intrepid MCMC algorithm. Finally, its utility is highlighted through a Bayesian parameter inference problem for a two-degree-of-freedom oscillator under free vibration.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118402"},"PeriodicalIF":7.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GPU-accelerated multi-phase, multi-resolution SPH method with ray tracing for laser powder bed fusion 基于gpu加速多相、多分辨率SPH的激光粉末床熔合射线追踪方法

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-25 DOI: 10.1016/j.cma.2025.118423

Yibo Ma , Zhilang Zhang , Christian Weißenfels , Minli Zhou , Lingxiao Ma , Xiaofei Tang , Moubin Liu

{"title":"GPU-accelerated multi-phase, multi-resolution SPH method with ray tracing for laser powder bed fusion","authors":"Yibo Ma , Zhilang Zhang , Christian Weißenfels , Minli Zhou , Lingxiao Ma , Xiaofei Tang , Moubin Liu","doi":"10.1016/j.cma.2025.118423","DOIUrl":"10.1016/j.cma.2025.118423","url":null,"abstract":"<div><div>Powder-scale simulation of laser powder bed fusion (LPBF) is increasingly vital for understanding, predicting, and controlling metallurgical defects. However, the complex multi-physics and multi-material interactions involved, along with high computational demands, pose significant challenges. This study presents the first multiphase smoothed particle hydrodynamics (SPH) simulation framework for LPBF under both low and high evaporation regimes, incorporating a multi-resolution particle strategy and ray tracing (RT). An adaptive particle refinement (APR) method, compatible with multiphase SPH and optimized for GPU acceleration, is developed to enhance computational efficiency of the multiphase model. The RT model is also optimized and integrated with the APR-GPU architecture, further improving performance. A physics-based wetting force model is introduced, along with a novel method for improving normal vector accuracy near the contact line. The proposed framework is validated through three benchmark cases and applied to simulate LPBF processes. The results demonstrate that the framework achieves high accuracy and efficiency in resolving key LPBF phenomena, including melt pool dynamics and keyhole formation.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118423"},"PeriodicalIF":7.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications 用于训练科学机器学习应用的两级重叠加性Schwarz预调节器

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-24 DOI: 10.1016/j.cma.2025.118400

Youngkyu Lee , Alena Kopaničáková , George Em Karniadakis

引用次数: 0

Generative emulation of chaotic dynamics with coherent prior 具有相干先验的混沌动力学生成仿真

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-24 DOI: 10.1016/j.cma.2025.118410

Juan Nathaniel, Pierre Gentine

{"title":"Generative emulation of chaotic dynamics with coherent prior","authors":"Juan Nathaniel, Pierre Gentine","doi":"10.1016/j.cma.2025.118410","DOIUrl":"10.1016/j.cma.2025.118410","url":null,"abstract":"<div><div>Data-driven emulation of nonlinear dynamics is challenging due to long-range skill decay that often produces physically unrealistic outputs. Recent advances in generative modeling aim to address these issues by providing uncertainty quantification and correction. However, the quality of generated simulation remains heavily dependent on the choice of conditioning prior. In this work, we present an efficient generative framework for nonlinear dynamics emulation, connecting principles of turbulence with diffusion-based modeling: Cohesion. Our method estimates large-scale coherent structure of the underlying dynamics as guidance during the denoising process, where small-scale fluctuation in the flow is then resolved. These coherent prior are efficiently approximated using reduced-order models, such as deep Koopman operators, that allow for rapid generation of long prior sequences while maintaining stability over extended forecasting horizon. With this gain, we can reframe forecasting as trajectory planning, a common task in reinforcement learning, where conditional denoising is performed once over entire sequences, minimizing the computational cost of autoregressive-based generative methods. Numerical evaluations on chaotic systems of increasing complexity, including Kolmogorov flow, shallow water equations, and subseasonal-to-seasonal climate dynamics, demonstrate Cohesion superior long-range forecasting skill that can efficiently generate physically-consistent simulations, even in the presence of partially-observed guidance.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118410"},"PeriodicalIF":7.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Configuration-decoupled concurrent topology optimization of heterogeneous lattice structures 异构晶格结构的构型解耦并发拓扑优化

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-24 DOI: 10.1016/j.cma.2025.118405

Xinze Shen , Changdong Zhang , Wenhe Liao, Dawei Li, Tingting Liu

{"title":"Configuration-decoupled concurrent topology optimization of heterogeneous lattice structures","authors":"Xinze Shen , Changdong Zhang , Wenhe Liao, Dawei Li, Tingting Liu","doi":"10.1016/j.cma.2025.118405","DOIUrl":"10.1016/j.cma.2025.118405","url":null,"abstract":"<div><div>Multi-configuration lattice structures have recently been introduced into structural optimization due to their broadly tunable physical properties. Traditional methods for multi-configuration lattice optimization employ extreme strategies of complete fusion or separation, leading to a trade-off between optimality and scalability that has not been fully addressed in the existing literature. The paper suggests decomposing the lattice library into pairs of lattices, through which multi-configuration lattice optimization is decoupled into the concurrent optimization of iso-value, combination category, and ratio within combination. A novel hybrid interpolation scheme is proposed to describe the effective mechanical behavior of the configuration-decoupled lattices. In this approach, polynomial models are employed to characterize the performance of individual lattice combinations, while the Uniform Multiphase Materials Interpolation model is used to integrate the contributions of all combinations. Benchmark experiments, including full-scale simulations, are conducted to validate the effectiveness of the framework. The proposed method enables rapid convergence to configuration layouts that align with the principal stress orientations. Compared to single- and dual-configuration designs, it achieves compliance reductions of 61.0 % and 26.2 %, respectively, approaching the performance of density-based topology optimization. Extended numerical experiments reveal the joint influence of resolution and configuration count on the overall performance. This method achieves a better trade-off between optimality and extensibility, enabling more flexible utilization of large lattice databases in practical engineering fields.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118405"},"PeriodicalIF":7.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Latent space modeling of parametric and time-dependent PDEs using neural ODEs 基于神经ode的参数化和时变偏微分方程的潜在空间建模

IF 7.3 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-09-23 DOI: 10.1016/j.cma.2025.118394

Alessandro Longhi, Danny Lathouwers, Zoltán Perkó

{"title":"Latent space modeling of parametric and time-dependent PDEs using neural ODEs","authors":"Alessandro Longhi, Danny Lathouwers, Zoltán Perkó","doi":"10.1016/j.cma.2025.118394","DOIUrl":"10.1016/j.cma.2025.118394","url":null,"abstract":"<div><div>Partial Differential Equations (PDEs) are central to science and engineering. Since solving them is computationally expensive, a lot of effort has been put into approximating their solution operator via both traditional and recently increasingly Deep Learning (DL) techniques. In this paper, we propose an autoregressive and data-driven method using the analogy with classical numerical solvers for time-dependent, parametric and (typically) nonlinear PDEs. We present how Dimensionality Reduction (DR) can be coupled with Neural Ordinary Differential Equations (NODEs) in order to learn the solution operator of arbitrary PDEs accounting both for (continuous) time and parameter dependency. The idea of our work is that it is possible to map the high-fidelity (i.e., high-dimensional) PDE solution space into a reduced (low-dimensional) space, which subsequently exhibits dynamics governed by a (latent) Ordinary Differential Equation (ODE). Solving this (easier) ODE in the reduced space allows avoiding solving the PDE in the high-dimensional solution space, thus decreasing the computational burden for repeated calculations for e.g., uncertainty quantification or design optimization purposes. The main outcome of this work is the importance of exploiting DR as opposed to the recent trend of building large and complex architectures: we show that by leveraging DR we can deliver not only more accurate predictions, but also a considerably lighter and faster DL model compared to existing methodologies.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"448 ","pages":"Article 118394"},"PeriodicalIF":7.3,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0