4.7 Article

A robust unsupervised neural network framework for geometrically nonlinear analysis of inelastic truss structures

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Interdisciplinary Applications

A novel deep unsupervised learning-based framework for optimization of truss structures

Hau T. Mai et al.

Summary: This paper proposes an efficient deep unsupervised learning-based framework for the design optimization of truss structures. By parameterizing the members' cross-sectional areas using a deep neural network, the framework aims to minimize the total structure weight and satisfy all optimization constraints. Through training the model with a combination of gradient optimization and backpropagation algorithm, the optimal weight of truss structures can be obtained without using other time-consuming metaheuristic algorithms.

ENGINEERING WITH COMPUTERS (2023)

Article Computer Science, Interdisciplinary Applications

Finite strain FE2 analysis with data-driven homogenization using deep neural networks

Nan Feng et al.

Summary: This paper presents a data-driven deep neural network (DNN) approach to accelerate FE2 analysis. By using DNN surrogate models for nonlinear homogenization, the computational burden can be reduced. Two training methods are compared, and the results show that Sobolev training achieves higher accuracy.

COMPUTERS & STRUCTURES (2022)

Article Computer Science, Artificial Intelligence

An adaptive surrogate model to structural reliability analysis using deep neural network

Qui X. Lieu et al.

Summary: This article introduces a simple and effective adaptive surrogate model using deep neural network for structural reliability analysis. The approach enhances accuracy by adding important boundary points to the global model and achieves precise failure probability assessment with only a small number of experiments.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Acoustics

Damage quantification in truss structures by limited sensor-based surrogate model

Seunghye Lee et al.

Summary: In this study, deep learning techniques were used for structural damage detection of truss structures by training deep neural networks to recognize response patterns of undamaged and damaged structures. The results demonstrated that the proposed surrogate model was able to accurately detect damaged states.

APPLIED ACOUSTICS (2021)

Article Engineering, Multidisciplinary

Smart constitutive laws: Inelastic homogenization through machine learning

Hernan J. Logarzo et al.

Summary: This work introduces an alternative formulation to concurrent multiscale models (CMMs) called smart constitutive laws (SCLs), which leverage advanced micromechanical modeling and machine learning techniques to boost computational efficiency and scalability for nonlinear and history-dependent materials. SCLs are suitable for arbitrary loading histories and enable the automatic generation of microstructurally-informed constitutive laws for solving macro-scale complex structures. The research findings suggest that SCLs can significantly improve computational homogenization for materials with arbitrary microstructures.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Engineering, Civil

Prediction of non-linear buckling load of imperfect reticulated shell using modified consistent imperfection and machine learning

Shaojun Zhu et al.

Summary: A modified method for estimating non-linear buckling load of an imperfect structure is proposed, introducing the shape of the first order linear buckling mode as the imperfection pattern. The similarity between the imperfection pattern and the structure deformation is highly relevant to the non-linear buckling load. A prediction method is suggested using machine learning techniques to avoid computational costs of multiple non-linear buckling analyses, with verification of its performance and a simplified formula for design based on the linear kernel.

ENGINEERING STRUCTURES (2021)

Article Mechanics

Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning

Xiaoying Zhuang et al.

Summary: This paper introduces a Deep Autoencoder based Energy Method (DAEM) for the bending, vibration, and buckling analysis of Kirchhoff plates. The DAEM utilizes higher-order continuity, integrates a deep autoencoder and the minimum total potential principle, and serves as an unsupervised feature learning method. It efficiently identifies patterns, minimizes total potential energy, extracts fundamental frequencies and critical buckling loads, alleviates gradient problems, and improves computational efficiency and generality through transfer learning.

EUROPEAN JOURNAL OF MECHANICS A-SOLIDS (2021)

Article Mechanics

An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates

Samir Khatir et al.

Summary: This paper proposes two-stage approaches to study damage detection, localization, and quantification in Functionally Graded Material (FGM) plate structures. It uses IsoGeometric Analysis (IGA) to model FGM plates and an Improved Artificial Neural Network using Arithmetic Optimization Algorithm (IANN-AOA) for damage quantification. The improved indicator shows high precision in predicting damaged elements and IANN-AOA provides more accurate results for damage quantification compared to IANNBCMO.

COMPOSITE STRUCTURES (2021)

Article Engineering, Multidisciplinary

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

Ehsan Haghighat et al.

Summary: The Physics-Informed Neural Network (PINN) framework combines physics with deep learning to solve PDEs and identify parameters. A nonlocal PINN approach with long-range interactions shows superior performance in solution accuracy and parameter inference compared to local approaches.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Mathematics, Applied

A machine learning-based surrogate model for optimization of truss structures with geometrically nonlinear behavior

Hau T. Mai et al.

Summary: A surrogate model based on deep neural network integrated with a differential evolution algorithm is developed for optimizing the design of geometrically nonlinear structures, significantly reducing computational costs while ensuring convergence.

FINITE ELEMENTS IN ANALYSIS AND DESIGN (2021)

Article Mechanics

Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures

H. Tran-Ngoc et al.

Summary: In this paper, a novel approach is proposed that combines the fast convergence speed of ANN with the global search capacity of EAs. This method ensures that the network possibly determines the best solution fast and avoids getting stuck in local minima by working parallel with EAs during the training process.

COMPOSITE STRUCTURES (2021)

Article Automation & Control Systems

A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications

Navid Zobeiry et al.

Summary: A physics-informed neural network is developed to solve conductive heat transfer PDEs with convective boundary conditions, improving the speed and accuracy of thermal analysis in manufacturing and engineering applications. By using physics-informed activation functions, heat transfer beyond training zone can be accurately predicted, making it a useful tool for real-time evaluation of thermal responses in a wide range of scenarios.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

Article Engineering, Multidisciplinary

Geometrical nonlinear problems of truss beam by base force element method

Yijiang Peng et al.

Summary: The base force element method (BFEM) is a new finite element method that deals with nonlinear problems based on the principle of complementary energy. This method is used to derive linear elastic and large elastic deformation models of plane truss elements, and expand them into a three-dimensional setting. Results show that using the U.L and T.L formats in BFEM can improve calculation efficiency when compared with traditional finite element software elements.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2021)

Article Engineering, Multidisciplinary

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

Ehsan Haghighat et al.

Summary: This study presents the application of Physics Informed Neural Networks (PINN) in solid mechanics, improving accuracy and convergence with a multi-network model and Isogeometric Analysis. The study demonstrates the importance of honoring physics in improving robustness and highlights the potential application of PINN in sensitivity analysis and surrogate modeling.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Engineering, Multidisciplinary

A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches

Wei Li et al.

Summary: This study establishes a neural network-based computational framework to characterize the finite deformation of elastic plates by incorporating known physical laws into the training process, reducing the data demand significantly. The accuracy of the modeling framework is carefully examined by applying it to different loading cases, showing satisfactory results with proper training strategies.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Mathematics, Applied

Recurrent neural networks as optimal mesh refinement strategies

Jan Bohn et al.

Summary: A recurrent neural network with a fixed number of trainable parameters can learn optimal mesh refinement algorithms for a wide variety of PDE problems, regardless of the desired accuracy and input size. The proposed algorithm is problem-independent and only requires the current numerical approximation to optimally refine the mesh, making it a provably optimal black-box mesh refinement tool.

COMPUTERS & MATHEMATICS WITH APPLICATIONS (2021)

Article Engineering, Multidisciplinary

Meshless physics-informed deep learning method for three-dimensional solid mechanics

Diab W. Abueidda et al.

Summary: Deep learning and the collocation method are merged to solve partial differential equations describing structures' deformation, offering a meshfree approach that avoids spatial discretization and data generation bottlenecks.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2021)

Article Computer Science, Interdisciplinary Applications

TOuNN: Topology Optimization using Neural Networks

Aaditya Chandrasekhar et al.

Summary: In this study, a new topology optimization method is proposed using neural networks to represent and optimize the density field, resulting in sharp and differentiable boundaries. The research demonstrates that the method is simple to implement and illustrates its application through 2D and 3D examples. Some unresolved challenges with the proposed framework are also summarized.

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2021)

Article Mechanics

A deep energy method for finite deformation hyperelasticity

Vien Minh Nguyen-Thanh et al.

EUROPEAN JOURNAL OF MECHANICS A-SOLIDS (2020)

Article Engineering, Civil

Enhanced method for the nonlinear structural analysis based on direct energy principles

Christopher Taube et al.

ENGINEERING STRUCTURES (2020)

Review Engineering, Civil

Review of Nonlinear Analysis and Modeling of Steel and Composite Structures

Huu-Tai Thai et al.

INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS (2020)

Article Engineering, Multidisciplinary

The neural particle method - An updated Lagrangian physics informed neural network for computational fluid dynamics

Henning Wessels et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

Article Engineering, Mechanical

Improved ANN technique combined with Jaya algorithm for crack identification in plates using XIGA and experimental analysis

S. Khatir et al.

THEORETICAL AND APPLIED FRACTURE MECHANICS (2020)

Article Engineering, Multidisciplinary

A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures

H. Tran-Ngoc et al.

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE (2020)

Article Engineering, Multidisciplinary

Multiscale topology optimization using neural network surrogate models

Daniel A. White et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)

Article Engineering, Multidisciplinary

Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network

F. Ghavamian et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)

Article Engineering, Multidisciplinary

Machine learning closures for model order reduction of thermal fluids

Omer San et al.

APPLIED MATHEMATICAL MODELLING (2018)

Article Multidisciplinary Sciences

A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis

Liang Liang et al.

JOURNAL OF THE ROYAL SOCIETY INTERFACE (2018)

Article Computer Science, Interdisciplinary Applications

Path following techniques for geometrically nonlinear structures based on Multi-point methods

Ali Maghami et al.

COMPUTERS & STRUCTURES (2018)

Review Computer Science, Interdisciplinary Applications

Background Information of Deep Learning for Structural Engineering

Seunghye Lee et al.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2018)

Article Engineering, Multidisciplinary

Modeling of thermotransport phenomenon in metal alloys using artificial neural networks

Seshasai Srinivasan et al.

APPLIED MATHEMATICAL MODELLING (2013)

Article Engineering, Civil

Analysis of trusses by total potential optimization method coupled with harmony search

Yusuf Cengiz Toklu et al.

STRUCTURAL ENGINEERING AND MECHANICS (2013)

Article Engineering, Multidisciplinary

Static analysis of beam structures under nonlinear geometric and constitutive behavior

P. Mata et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2007)

Article Mathematics, Applied

Nonlinear positional formulation for space truss analysis

M. Greco et al.

FINITE ELEMENTS IN ANALYSIS AND DESIGN (2006)

Article Computer Science, Interdisciplinary Applications

Nonlinear analysis and optimal design of structures via force method and genetic algorithm

A Kaveh et al.

COMPUTERS & STRUCTURES (2006)

Article Computer Science, Interdisciplinary Applications

Nonlinear analysis of trusses through energy minimization

YC Toklu

COMPUTERS & STRUCTURES (2004)