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

Parametric Gaussian quadratures for discrete unified gas kinetic scheme 离散统一气体动力学格式的参数高斯正交

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-12 DOI: 10.1016/j.cma.2025.118053

Lu Wang, Hong Liang, Jiangrong Xu

{"title":"Parametric Gaussian quadratures for discrete unified gas kinetic scheme","authors":"Lu Wang, Hong Liang, Jiangrong Xu","doi":"10.1016/j.cma.2025.118053","DOIUrl":"10.1016/j.cma.2025.118053","url":null,"abstract":"<div><div>The discrete unified gas kinetic scheme (DUGKS) has emerged as a promising Boltzmann solver capable of effectively capturing flow physics across all Knudsen numbers. However, simulating rarefied flows at high Knudsen numbers remains computationally demanding. This paper introduces a parametric Gaussian quadrature (PGQ) rule designed to improve the computational efficiency of DUGKS. The PGQ rule employs Gaussian functions for weighting and introduces several novel forms of higher-dimensional Gauss–Hermite quadrature. Initially, the velocity space is mapped to polar or spherical coordinates using a parameterized integral transformation method, which converts multiple integrals into repeated parametric integrals. Subsequently, Gaussian points and weight coefficients are computed based on the newly defined parametric weight functions. The parameters in PGQ allow the distribution of Gaussian points to be adjusted according to computational requirements, addressing the limitations of traditional Gaussian quadratures where Gaussian points are difficult to match the distribution of real particles in rarefied flows. To validate the proposed approach, numerical examples across various Knudsen numbers are provided. The simulation results demonstrate that PGQ offers superior computational efficiency and flexibility compared to the traditional Newton–Cotes rule and the half-range Gaussian Hermite rule, achieving computational efficiency that is tens of times higher than that of the Newton–Cotes method. This significantly enhances the computational efficiency of DUGKS and augments its ability to accurately simulate rarefied flow dynamics.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"443 ","pages":"Article 118053"},"PeriodicalIF":6.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936699","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

Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction 非线性结构地震反应预测的本构模型约束物理信息神经网络框架

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-12 DOI: 10.1016/j.cma.2025.118079

Yongxin Wu , Zhanpeng Yin , Yufeng Gao , Shangchuan Yang , Yue Hou

{"title":"Constitutive model-constrained physics-informed neural networks framework for nonlinear structural seismic response prediction","authors":"Yongxin Wu , Zhanpeng Yin , Yufeng Gao , Shangchuan Yang , Yue Hou","doi":"10.1016/j.cma.2025.118079","DOIUrl":"10.1016/j.cma.2025.118079","url":null,"abstract":"<div><div>Seismic response prediction presents a significant challenge in earthquake engineering, particularly in balancing computational efficiency with physical accuracy. Traditional numerical methods are computationally expensive for performing large-scale nonlinear analyses, while data-driven machine learning approaches, though computational efficiency, often lack physical constraints and sufficient training data. Physics-Informed Neural Networks (PINNs), an emerging approach that integrates physical laws with deep learning techniques to solve complex scientific and engineering problems, show great potential. However, incorporating nonlinear constitutive models to accurately describe the structural behavior under seismic loading remains a challenge. In this study, a new framework, constitutive model-constrained physics-informed neural networks (CM-PINNs), is proposed to address this issue. This framework enhances prediction accuracy and physical interpretability by incorporating nonlinear constitutive constraints into the loss function. It also uses a fully connected skip LSTM architecture and implements an adaptive loss weight initialization strategy. Numerical validation demonstrates the superior performance of the CM-PINNs framework in simulating single-degree-of-freedom nonlinear seismic responses. Under limited training data conditions, CM-PINNs demonstrates notably superior performance compared to existing methods such as physics-informed multi-LSTM networks (PhyLSTM). Additionally, the scalability of CM-PINNs is verified through its application to multi-layer shear building structures. The results demonstrate that CM-PINNs provide a computationally efficient and reliable approach for seismic response prediction.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"443 ","pages":"Article 118079"},"PeriodicalIF":6.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936701","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

Conditional uncertainty propagation of stochastic dynamical structures considering measurement conditions 考虑测量条件的随机动力结构的条件不确定性传播

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-09 DOI: 10.1016/j.cma.2025.118065

Feng Wu, Yuelin Zhao, Li Zhu

{"title":"Conditional uncertainty propagation of stochastic dynamical structures considering measurement conditions","authors":"Feng Wu, Yuelin Zhao, Li Zhu","doi":"10.1016/j.cma.2025.118065","DOIUrl":"10.1016/j.cma.2025.118065","url":null,"abstract":"<div><div>How to accurately quantify the uncertainty of stochastic dynamical responses affected by uncertain loads and structural parameters is an important issue in structural safety and reliability analysis. In this paper, the conditional uncertainty propagation problem for the dynamical response of stochastic structures considering the measurement data with random error is studied in depth. A method for extracting the key measurement condition, which holds the most reference value for the uncertainty quantification of response, from the measurement data is proposed. Considering the key measurement condition and employing the principle of probability conservation and conditional probability theory, the quotient-form expressions for the conditional mean, conditional variance, and conditional probability density function of the stochastic structural dynamical response are derived and are referred to as the key conditional quotients (KCQ). A numerical method combining the non-equal weighted generalized Monte Carlo method, Dirac function smoothing technique, and online-offline coupled computational strategy is developed for calculating KCQs. Three linear/nonlinear stochastic dynamical examples are used to verify that the proposed KCQ method can efficiently and accurately quantify the uncertainty of the structural response considering measurement conditions. The examples also compare the traditional non-conditional uncertainty propagation results with the conditional uncertainty propagation results given by KCQs, indicating that considering measurement conditions can significantly reduce the uncertainty of the stochastic dynamical responses, providing a more refined statistical basis for structural safety and reliability analysis.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 118065"},"PeriodicalIF":6.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922614","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

Multi-material topology optimization based on finite strain subloading surface nonlocal elastoplasticity 基于有限应变加载面非局部弹塑性的多材料拓扑优化

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-09 DOI: 10.1016/j.cma.2025.118038

Jike Han , Yuki Yamakawa , Kazuhiro Izui , Shinji Nishiwaki , Kenjiro Terada

{"title":"Multi-material topology optimization based on finite strain subloading surface nonlocal elastoplasticity","authors":"Jike Han , Yuki Yamakawa , Kazuhiro Izui , Shinji Nishiwaki , Kenjiro Terada","doi":"10.1016/j.cma.2025.118038","DOIUrl":"10.1016/j.cma.2025.118038","url":null,"abstract":"<div><div>This study is dedicated to the multi-material topology optimization formulation (MMTO) for finite strain nonlocal elastoplasticity. The subloading surface model is newly incorporated into the primal problem to achieve the gradual change of the deformation process from pure elastic to material-specific plastic hardening. The stress–strain relationship of the model is a smooth continuous function, which is beneficial for elastoplastic topology optimization since the resulting continuous tangent is used in the adjoint problem to determine the sensitivity. Also, the nonlocal plastic modeling is introduced to resolve mesh-dependency issues in the evolution of plastic deformation. In addition, in order to maintain computational stability and to avoid unrealistic plastic deformation occurring in voids (ersatz material), the concept of interpolating energy densities is introduced, by which linearly elastic material is chosen to represent voids. The continuous adjoint method is employed to derive the governing equations and sensitivity of the adjoint problem, and the resulting equations are valid at any position, boundary, or time in the continuum without relying on any discretization. An arbitrary number of design variables can be considered for multiple materials in the optimization problem, and by referring to the derived sensitivity, the multiple reaction–diffusion equations are solved to update the material distribution and configuration. The first numerical example demonstrates the “oscillation of deformation states” caused by the conventional plastic model and shows how the subloading surface model effectively resolves this issue, achieving stable optimization processes. Also, the second example presents the unconventional deformation magnitude-dependent stiffness maximization problems with multiple materials, in which the optimal designs are realized by referring to the same elastic but different plastic material properties.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 118038"},"PeriodicalIF":6.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929250","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

A quantitative comparison of high-order asymptotic-preserving and asymptotically-accurate IMEX methods for the Euler equations with non-ideal gases 非理想气体Euler方程的高阶渐近保持和渐近精确IMEX方法的定量比较

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-09 DOI: 10.1016/j.cma.2025.118037

Giuseppe Orlando , Sebastiano Boscarino , Giovanni Russo

{"title":"A quantitative comparison of high-order asymptotic-preserving and asymptotically-accurate IMEX methods for the Euler equations with non-ideal gases","authors":"Giuseppe Orlando , Sebastiano Boscarino , Giovanni Russo","doi":"10.1016/j.cma.2025.118037","DOIUrl":"10.1016/j.cma.2025.118037","url":null,"abstract":"<div><div>We present a quantitative comparison between two different Implicit–Explicit Runge–Kutta (IMEX-RK) approaches for the Euler equations of gas dynamics, specifically tailored for the low Mach limit. In this regime, a classical IMEX-RK approach involves an implicit coupling between the momentum and energy balance so as to avoid the acoustic CFL restriction, while the density can be treated in a fully explicit fashion. This approach leads to a mildly nonlinear equation for the pressure, which can be solved according to a fixed point procedure. An alternative strategy consists of employing a semi-implicit temporal integrator based on IMEX-RK methods (SI-IMEX-RK). The stiff dependence is carefully analyzed, so as to avoid the solution of a nonlinear equation for the pressure also for equations of state (EOS) of non-ideal gases. The spatial discretization is based on a Discontinuous Galerkin (DG) method, which naturally allows high-order accuracy. The asymptotic-preserving (AP) and the asymptotically-accurate (AA) properties of the two approaches are assessed on a number of classical benchmarks for ideal gases and on their extension to non-ideal gases.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 118037"},"PeriodicalIF":6.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929251","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

Accelerating cell topology optimisation by leveraging similarity in the parametric input space 通过利用参数输入空间中的相似性来加速单元拓扑优化

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-09 DOI: 10.1016/j.cma.2025.118044

A. Martínez-Martínez , D. Muñoz , J.M. Navarro-Jiménez , O. Allix , F. Chinesta , J.J. Ródenas , E. Nadal

{"title":"Accelerating cell topology optimisation by leveraging similarity in the parametric input space","authors":"A. Martínez-Martínez , D. Muñoz , J.M. Navarro-Jiménez , O. Allix , F. Chinesta , J.J. Ródenas , E. Nadal","doi":"10.1016/j.cma.2025.118044","DOIUrl":"10.1016/j.cma.2025.118044","url":null,"abstract":"<div><div>The design of high-resolution topology-optimised (TO) structures is important for many industrial and medical applications because of their better mechanical performance under different load conditions. Traditional density-based TO methods, like the Solid Isotropic Material with Penalisation (SIMP) method, can produce detailed designs but are very computationally expensive, especially for fine meshes. While surrogate models using neural networks can speed up the process, they often lack generality and can create discontinuities, making them less effective for solving new problems.</div><div>This study addresses these issues by introducing a method to speed up cell-level TO within a 2-Level framework, where large structures are built by combining optimised square cells. A data-driven instance-based model provides a better starting point for the standard SIMP-based optimiser, placing it closer to a local minimum and reducing computation time. To avoid the generality problems of other methods, the instance-based model uses a dataset expanded through two strategies: context-based data creation, which generates specific samples for the problem, and data augmentation, which increases dataset size without extra computation.</div><div>Two similarity metrics, vector-based and energy-based, are used to measure how close the input parameters are. Both metrics are effective, but the energy-based metric is expected to work better in 3D cases, where higher-dimensional input spaces make other approaches less reliable. This methodology addresses important challenges associated with existing instance-based models, enhancing the speed and applicability of high-resolution TO.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 118044"},"PeriodicalIF":6.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922613","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

Physics-encoded convolutional attention network for forward and inverse analysis of spatial-temporal parabolic dynamics considering discontinuous heterogeneity 考虑不连续非均质性的时空抛物动力学正反分析的物理编码卷积注意网络

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-08 DOI: 10.1016/j.cma.2025.118025

Xi Wang, Zhen-Yu Yin

{"title":"Physics-encoded convolutional attention network for forward and inverse analysis of spatial-temporal parabolic dynamics considering discontinuous heterogeneity","authors":"Xi Wang, Zhen-Yu Yin","doi":"10.1016/j.cma.2025.118025","DOIUrl":"10.1016/j.cma.2025.118025","url":null,"abstract":"<div><div>Physics-informed neural network (PINN) prevails as a differentiable computational network to unify forward and inverse analysis of partial differential equations (PDEs). However, PINN suffers limited ability in complex transient physics with nonsmooth heterogeneity, and the training cost can be unaffordable. To this end, we propose a novel framework named physics-encoded convolutional attention network (PECAN). Leveraging physics-encoded convolution kernels, automatic differentiations are circumvented when deriving spatial derivatives. The truncated self-attention is built to handle variable temporal sequences in parallel. The positional encoding is avoided by considering temporal evolution direction and step size. PECAN enables a global-range consideration of temporal data and significantly reduces sequential operations. Encoding physics knowledge into the network greatly simplifies the architecture and reduces blackbox parameters. To conduct a comprehensive investigation of different physics-encoded architectures for the first time, the parabolic PDE that describes a broad scope of physical phenomena is investigated in depth. The PECAN proves to be four orders of magnitude faster and more accurate than PINNs for inverse analysis. It can readily handle discontinuous heterogeneity containing multiple distinct materials with discontinuous material interfaces, while PINNs fail. Accurate parameters of discontinuous heterogeneous materials (relative errors < 2 %) are recovered even with 50 % Gaussian noise or sparse data with non-Gaussian noise. Superior performance warrants further development of this novel framework.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 118025"},"PeriodicalIF":6.9,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917924","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

A consistent diffuse-interface finite element approach to rapid melt–vapor dynamics with application to metal additive manufacturing 快速熔气动力学的一致扩散界面有限元方法及其在金属增材制造中的应用

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-08 DOI: 10.1016/j.cma.2025.117985

Magdalena Schreter-Fleischhacker , Nils Much , Peter Munch , Martin Kronbichler , Wolfgang A. Wall , Christoph Meier

{"title":"A consistent diffuse-interface finite element approach to rapid melt–vapor dynamics with application to metal additive manufacturing","authors":"Magdalena Schreter-Fleischhacker , Nils Much , Peter Munch , Martin Kronbichler , Wolfgang A. Wall , Christoph Meier","doi":"10.1016/j.cma.2025.117985","DOIUrl":"10.1016/j.cma.2025.117985","url":null,"abstract":"<div><div>Metal additive manufacturing via laser-based powder bed fusion (PBF-LB/M) faces performance-critical challenges due to complex melt pool and vapor dynamics, often oversimplified by computational models that neglect crucial aspects, such as vapor jet formation. To address this limitation, we propose a consistent computational multi-physics mesoscale model to study melt pool dynamics, laser-induced evaporation, and vapor flow. In addition to the evaporation-induced pressure jump, we also resolve the evaporation-induced volume expansion and the resulting velocity jump at the liquid–vapor interface. We use an anisothermal incompressible Navier–Stokes solver extended by a conservative diffuse level-set framework and integrate it into a matrix-free adaptive finite element framework. To ensure accurate physical solutions despite extreme density, pressure and velocity gradients across the diffuse liquid–vapor interface, we employ consistent interface source term formulations developed in our previous work. These formulations consider projection operations to extend solution variables from the sharp liquid–vapor interface into the computational domain. Benchmark examples, including film boiling, confirm the accuracy and versatility of the model. As a key result, we demonstrate the model’s ability to capture the strong coupling between melt and vapor flow dynamics in PBF-LB/M based on simulations of stationary laser illumination on a metal plate. Additionally, we show the derivation of the well-known Anisimov model and extend it to a new hybrid model. This hybrid model, together with consistent interface source term formulations, especially for the level-set transport velocity, enables PBF-LB/M simulations that combine accurate physical results with the robustness of an incompressible, diffuse-interface computational modeling framework.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 117985"},"PeriodicalIF":6.9,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917925","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

An adaptive cycle jump method for elasto-plastic phase field modeling addressing fatigue crack propagation 一种求解疲劳裂纹扩展的弹塑性相场模型自适应循环跳变方法

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-08 DOI: 10.1016/j.cma.2025.118074

Jiawei Li , Yanan Hu , Ni Ao , Hongchen Miao , Xu Zhang , Guozheng Kang , Qianhua Kan

{"title":"An adaptive cycle jump method for elasto-plastic phase field modeling addressing fatigue crack propagation","authors":"Jiawei Li , Yanan Hu , Ni Ao , Hongchen Miao , Xu Zhang , Guozheng Kang , Qianhua Kan","doi":"10.1016/j.cma.2025.118074","DOIUrl":"10.1016/j.cma.2025.118074","url":null,"abstract":"<div><div>In recent years, the phase field method has been widely used in the simulation of fatigue crack propagation. However, fine mesh and cyclic simulation cycle by cycle significantly increase the computational cost of phase field simulation, which poses challenges in simulating the entire process of fatigue crack propagation. This paper proposes a cycle jump method considering the effect of plasticity at the crack tip, enabling accelerated simulations of fatigue crack propagation in elasto-plastic materials. In this method, fatigue crack propagation is accelerated through cycle jump prediction of displacement field and phase field variables, while the plastic strain accumulation at the crack tip is considered by the prediction of displacement field variables. An adaptive algorithm is developed to automatically adjust the cycle jump size based on the phase field evolution. The effectiveness of the proposed method is verified by several numerical examples. The results show that the proposed method ensures computational accuracy while significantly enhancing efficiency.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 118074"},"PeriodicalIF":6.9,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922612","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

Data-driven reduced-order models for port-Hamiltonian systems with operator inference 带算子推理的port- hamilton系统的数据驱动降阶模型

IF 6.9 1区工程技术

Computer Methods in Applied Mechanics and Engineering Pub Date : 2025-05-07 DOI: 10.1016/j.cma.2025.118042

Yuwei Geng , Lili Ju , Boris Kramer , Zhu Wang

{"title":"Data-driven reduced-order models for port-Hamiltonian systems with operator inference","authors":"Yuwei Geng , Lili Ju , Boris Kramer , Zhu Wang","doi":"10.1016/j.cma.2025.118042","DOIUrl":"10.1016/j.cma.2025.118042","url":null,"abstract":"<div><div>Hamiltonian operator inference has been developed in Sharma et al. (2022) to learn structure-preserving reduced-order models (ROMs) for Hamiltonian systems. The method constructs a low-dimensional model using only data and knowledge of the functional form of the Hamiltonian. The resulting ROMs preserve the intrinsic structure of the system, ensuring that the mechanical and physical properties of the system are maintained. In this work, we extend this approach to port-Hamiltonian systems, which generalize Hamiltonian systems by including energy dissipation, external input, and output. Based on snapshots of the system’s state and output, together with the information about the functional form of the Hamiltonian, reduced operators are inferred through optimization and are then used to construct data-driven ROMs. To further alleviate the complexity of evaluating nonlinear terms in the ROMs, a hyper-reduction method via discrete empirical interpolation is applied. Accordingly, we derive error estimates for the ROM approximations of the state and output. Finally, we demonstrate the structure preservation, as well as the accuracy of the proposed port-Hamiltonian operator inference framework, through numerical experiments on a linear mass–spring-damper problem and a nonlinear Toda lattice problem.</div></div>","PeriodicalId":55222,"journal":{"name":"Computer Methods in Applied Mechanics and Engineering","volume":"442 ","pages":"Article 118042"},"PeriodicalIF":6.9,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911702","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