The finite element analysis results obtained from trialing several combinations of the SPT-N = 40's Young's modulus and cohesion were used to train the machine learning model, resulting in a heat map/contour of predicted deflections over the Young's modulus and cohesion coordinates. Sep 1, 2023 · Request PDF | On Sep 1, 2023, Suhan Kim and others published Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation | Find, read and Mar 1, 2022 · We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations (PDEs). Currently, literature on deep learning-enabled finite element analysis for pipelines SCC prediction is scarce, offering limited insights into this emerging approach and lack of comprehensive review. In this research, we implemented a deep learning model by training it on I/O data from simplified Jun 26, 2024 · Microstructures in asphalt, often resembling bee structures, are pivotal in influencing asphalt performance and, by extension, sustainable fuel production. MathSciNet MATH Google Scholar Jul 7, 2023 · Despite their different fields of application, Finite Element Analysis (FEA) and Deep Learning are interconnected through foundational mathematics, most notably in linear algebra and multivariable Deep learning (DL) with integration of FEA lever- Finite element method; ML: Machine learning; DL: Deep learning; EGIG: European gas pipeline inci- Jan 23, 2024 · Deep learning (DL) with integration of FEA leverages large-scale data and learn complex nonlinear patterns for SCC prediction. • Finite Concatenated Element (FCE) module: the FCE module allows connecting multiple BRNN elements in order to assemble models of complex systems. Yang and N. 107735 Owing to the challenge of capturing the dynamic behaviour of metal experimentally, high-precision numerical simulations have become essential for analysing dynamic characteristics. The results Oct 15, 2023 · Similarly, as HDG schemes are mixed methods, their formulation (10) involves vector-valued finite element spaces and use of a traced finite element space [57] on the mesh skeleton; that is, the underlying numerical method is significantly different than those used for the solution of the advection equation (cf. 13\times 10^{-7}$ RIU. Apr 10, 2023 · In this paper, a data-driven multiscale finite-element method (data-driven FE2) is proposed using a deep neural network (DNN) and proper orthogonal decomposition (POD) to describe nonlinear heterogeneous materials. pp. This method may enable real-time stress analysis by leveraging machine learning (ML) algori-thms. May 28, 2022 · In this article, an inversion model that can realize quantitative detection is proposed by combing the scaled boundary finite element method (SBFEM) with deep learning. ≤. Moreover, model simplification and parameter uncertainty usually lead to significant errors. J R Soc Interface 15(138):20170844. This paper presents a framework of the nonlinear finite element based on Jan 28, 2024 · Owing to the challenge of capturing the dynamic behaviour of metal experimentally, high-precision numerical simulations have become essential for analysing dynamic characteristics. The complex constitutive responses of sand under various loading paths can be reproduced by leveraging the powerful learning capacity of the DLM. The results show that for the same structural analysis problem, our surrogate model can predict the elastic strain and stress distributions with accuracy of 93. 2022. The Monte Carlo simulation (MCS) and perturbation methods have been mainly utilized for the conduction of stochastic finite element analysis (FEA). for simulation purposes [48]. 2 . Specifically, based on the large dataset obtained using the finite element method, the buckling strength of biological staggered composites was rapidly predicted using a machine learning algorithm. F. The Oct 14, 2020 · The hierarchical deep-learning neural network (HiDeNN) is systematically developed through the construction of structured deep neural networks (DNNs) in a hierarchical manner, and a special case of HiDeNN for representing Finite Element Method (or HiDeNN-FEM in short) is established. Gu}, journal={Advanced Theory and Simulations}, year={2020}, url={https Point-Cloud-based Deep Learning Models for Finite Element Analysis Meduri Venkata Shivaditya∗ , Francesca Bugiotti∗, Frédéric Magoulès† arXiv:2211. Courier Corporation, North Chelmsford. In: Materials Today Communications, Vol. 7%, respectively. doi: 10. The pressure vessel contains dry electronics, power sources Mar 11, 2024 · Deep learning has become a crucial instrument for medical research in recent years. 2021. READ FULL TEXT Feb 7, 2022 · Our current work aims to confirm the identification and growth modalities of microfractures on X-ray computed tomography images. 5% and94. The model takes nodal coordinates and Poisson’s ratio of eight-node and four-node quadrilateral plane stress elements as the input. mtcomm. 2. They require extremely high numerical accuracy, which is difficult to obtain by other numerical methods. 1098/rsif. Despite these improvements, certain challenges still remain. The pressure vessel contains dry electronics, power sources, and other sensors that cannot be flooded. 107233 Corpus ID: 263809014; Inverse design of polymer alloys using deep learning based on self-consistent field analysis and finite element analysis Jan 25, 2023 · Interpolation is done using piecewise elements as supported by FEniCS. 107735 , 10. 1016/j. Jul 25, 2021 · Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. 2024 Jan 28;17(3):643. Jan 28, 2024 · finite element (FE) model, and the deep learning (DL) model was employed for result analysis. engfailanal. The connections between neurons in the architecture mimic the Finite Element connectivity graph when applying mesh refinements. The proposed models show promise in automatizing the analysis process of finite element simulations. Hu}, journal={Computers and Geotechnics}, year={2023}, url={https://api Abstract. In this study, calculation accuracy was improved by analysing the impact of constitutive models using the finite element (FE) model, and the deep learning (DL) model was employed for result analysis. The two approaches are effective in analyzing stability for each specific excavation stage. Specifically, simulation requires the simultaneous trade-off of: 1. Introduction. In this work, the deep learning approach is used to generate stiffness matrices for 2D 8-node quadrilateral solid finite element. Jun 9, 2021 · The self-adaptive hp-Finite Element Method (FEM) has been developed for many years by the community of applied mathematicians working in the field of numerical analysis [3,4,5,6, 9]. The stress multiaxiality (M) is defined as the ratio of the hydrostatic stress (negative pressure) by the von Mises deviatoric stress. Google Scholar Wendland H (1999) Meshless Galerkin methods using radial basis functions. Engineers use this method to reduce the number of physical prototypes and experiments, and to optimize components in their design phase to develop better products, faster. Two-step optimization schemes are further developed to allow for the capabilities of r-adaptivity and easy integration with any existing FE solver. The deep learned finite elements practically pass the patch tests and the zero energy mode tests. 5. During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. Finite-element analysis (FEA) for structures has been broadly used One of the biggest hurdles in developing an innovative, intelligent failure analysis system that caters to the needs of the manufacturing industry, is the lack of manufacturing data to train a deep learning driven system. 20170844 , 10. A search procedure for optimal bending directions is implemented through deep Jan 23, 2024 · DOI: 10. X. R. However, patient-specific FEA models usually require complex procedures to set up and long computing times to …. In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simulation process. Jan 3, 2024 · In this study, a novel framework for the finite element (FE) analysis of geotechnical engineering problems is proposed, in which a deep learning (DL) model is employed to depict the constitutive Jan 28, 2024 · Accurate Finite Element Simulations of Dynamic Behaviour: Constitutive Models and Analysis with Deep Learning Materials (Basel) . We worked in the automatic identification by deep learningDeep learning modalities and finite element analysisFinite element analysis in Apr 1, 2020 · Assisted by image-based finite element analysis and deep learning, a data-driven approach is proposed in this study for designing phononic crystals. Apr 11, 2023 · Numerical examples of 2D and 3D demonstrate that the proposed nonlinear HiDeNN-FEM with r-adaptivity provides much higher accuracy than regular FEM and significantly reduces element distortion and suppresses the hourglass mode. Sep 1, 2023 · In [19], a so-called Deep Learning Discrete Calculus (DLDC) was proposed, which uses the knowledge from discrete numerical methods, such as finite difference and finite element methods, to interpret the deep learning algorithms. A toy example of interpolation is shown in Fig. 1 a. Two models are here presented: the Point-Net classification model and the Dynamic Graph Convolutional Neural Net model. To our knowledge, this work is the first `DL-FE Apr 15, 2021 · From a conceptual perspective, LE takes the role that element types and shape functions have in classical finite element analysis. n +1. The Dec 1, 2020 · Deep learning has been applied to construct surrogate models [26], [27] and constitutive models for material nonlinear finite element analysis [28], [29]. (2019) utilized the finite element analysis to calculate the basal heave stability and contrasted their findings with the traditional limit equilibrium methods. ABSTRACT During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. 2017. This framework consists of two deep learning models—one aimed at generating phase separation structures, and the other at predicting properties from Nov 18, 2022 · Both trained point-cloud deep learning models performed well on experiments with finite element analysis arising from automotive industry. It has been Mar 12, 2022 · After presenting the mechanical model, by using Finite Element Analysis (FEA), a deep learning neural network model is developed for small-scale structures and the capability of this model in Sep 1, 2023 · In this study, a deep learning framework for multiscale finite element analysis (FE 2) is proposed. Stress analysis is the hallmark of mechanics. 4. To overcome the inefficiency of the concurrent classical FE 2 method induced by the repetitive analysis at each macroscopic integration points, the distance-minimizing data-driven computational mechanics is adopted for the FE 2 analysis. FENA is a deep-learning-based framework for the simulation of physical systems. First, the DLFEA needs Dec 1, 2023 · For the stress data, the results of finite element analysis are used, in particular, the analysis results of average stresses over the system under the small-enforced-displacement condition. The results showed that FE simulations with these models effectively capture the elastic-plastic re-sponse, and the ZA model exhibits the highest accuracy, with a . The geometric multiscale finite element method is an intuitive and simple linear model for multiscale structures that makes no assumptions over the distribution of geometric features in the small scale. complement the finite element studies with machine learning and provide several basic examples. 6 Results of Back Analysis-Phase 1. In this chapter, we discuss a method to improve the accuracy of the FEM solution with a small number of Feb 1, 2024 · For ease of reading, the first group models were named as “Finite Element no Segmented Lesion” (FE-no-SL), and the second group as “Finite Element with Segmented Lesion” (FE-with-SL). In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simu-lation process. 0. The objective is to classify automatically the results of the simulations without tedious human intervention. 2024. Proof Semantic Scholar extracted view of "A new adaptive finite element analysis using deep learning" by Shogo Minami et al. FENA combines the network computational efficiency with the FE method flexibility. Apr 15, 2022 · A combined finite element and hierarchical deep learning approach for structural health monitoring: test on a pin-joint composite truss structure Mech Syst Signal Process , 157 ( 2021 ) , p. The proposed method is used to develop 4- and 8-node 2D solid finite elements. An accuracy of 79. 1080/19942060. Outcome measure and analysis. Soc. In this study, a deep learning framework for multiscale finite element analysis (FE 2) is proposed. 3. In HiDeNN-FEM, weights and biases are functions of the nodal positions, hence the training process in HiDeNN Oct 26, 2023 · This paper presents a novel approach to designing high-performance architectured ceramics by integrating machine learning (ML) techniques and finite element analysis (FEA) data. This paper presents FEMa—a finite element Mar 1, 2024 · Finite element model and sensitivity analysis2. Finite element (FE The comprehensive finite-element simulations indicate that it is possible to achieve remarkable sensing performances such as wavelength sensitivity (WS) and figure of merit (FOM) as high as 123,000 nm/RIU and 683 RIU1, respectively, and extremely low value of wavelength resolution (WR) about $8. Jul 1, 2024 · This paper integrates finite element (FE) and deep learning (DL) methods to predict the bond stress–slip behavior of reinforcing bars in concrete under static and dynamic loading. hal-04142075 Jun 6, 2022 · During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. Speaker: Panos Pantidis (New York University Abu Dhabi, United Arab Emirates)Title: Accelerating FEM with machine learning: an introduction to the Integrated May 3, 2022 · Deep learning Finite element analysis Boundary value problem ABSTRACT Owing to the complicated mechanical behaviors of soils, their constitutive models often involve obscure formu- Jan 18, 2023 · Using computer simulation tools such as finite element analysis (FEA) to perform material stress analysis is a common design method in engineering practice. Research output: Contribution to journal › Article › peer-review Mar 16, 2019 · Machine learning has played an essential role in the past decades and has been in lockstep with the main advances in computer technology. Jul 1, 2024 · DOI: 10. In this paper, a novel method combining finite element analysis with machine learning is proposed. Both trained point-cloud deep Jul 25, 2021 · Finite element analysis (FEA) has been widely used to predict the biomechanical performance of various dental applications such as orthodontic tooth movement, implant components, and peri-implant bone. % accuracy improvement com- Jun 6, 2022 · During experimentation, we observed that a Deep Learning-based surrogate outperforms other regression models on such sparse data. This research presents a new model called FEM-DCNN that integrates the Finite Element Method (FEM), Deep Auto Encoder Algorithm (DAE), and Convolutional Neural Network (CNN) techniques. 00049. This functionality serves two main roles: 1) it allows combining Dec 1, 2023 · For the stress data, the results of finite element analysis are used, in particular, the analysis results of average stresses over the system under the small-enforced-displacement condition. 2022 21st International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Oct 2022, Chizhou, China. sun@bme. This matches the element order of the finite element method used in these simulations, which has shown acceptable accuracy. In the MWA treatment, the antennas were positioned around the cancer tissue in equilateral triangle formation, with the half-slot side of the antennas directed at the triangle center to concentrate the microwave energy. To overcome the inefficiency of the concurrent classical FE 2 method induced by the repetitive analysis at each macroscopic integration points, the distance-minimizing data-driven computational mechanics is adopted for the FE 2 analysis. An auto-encoder is trained to extract the topological features from sample images. The finite element (FE) is activated through an iterative procedure to improve the solution accuracy without mesh refinement. compgeo. The pyLabFEA package introduces a simple version of FEA for solid mechanics and elastic-plastic materials, which is fully written in Python. Interface , 15 ( 2018 ) , p. Second order interpolation is done for velocities and first order is done for pressures. & Yu, D. Gautam Abstract The finite element method (FEM) is a well-known method for numerically solving partial differential equations (PDEs) over a physical domain. Jul 28, 2023 · Stress evaluation plays a pivotal role in the design of material systems, often accomplished through the finite element method (FEM) for intricate structures. 1 Stability Analysis for Linearized Finite Element Scheme Based on Backward Euler Format . Computer science-based mathematics have been extensively used in research to identify and forecast various diseases. Nath, Dipjyoti Nath, and Sachin S. Theorem 1 . Finite element model and verification. In 2015 12th International Bhurban Conference on Applied Inverse design of polymer alloys using deep learning based on self-consistent field analysis and finite element analysis. Sep 1, 2023 · Several approaches have been attempted to obtain theoretical behavioral data required for designing soft grippers, which have recently gained attention in the industry. Education Jun 6, 2022 · The Deep Learning-based model is utilized to replace the costly Finite Element Analysis-based simulation process and on the sparser data, the DL-based surrogate performs much better than other regression models. 1. 10073v1 [math. The residual from finite element methods and custom loss functions from neural networks are merged to form the algorithm. The concurrent classical FE2 needs the iterative calculations of microscopic boundary-value problem for representative volume element (RVE) at all integration points of the May 1, 2024 · Goh et al. The hierarchical deep-learning neural network (HiDeNN) (Zhang et al. This research proposes a triple-antenna scheme of three coaxial half slot antennas (CHSA) for minimally invasive hepatic microwave ablation (MWA). Both trained point-cloud deep Jun 6, 2024 · To showcase the effectiveness and versatility of the introduced concepts, they are applied to the transient simulation of homogeneous rods and inhomogeneous beams. We begin with a brief introduction of the traditional FEA process Dec 10, 2015 · informed deep learning for stochastic analysis of heterogeneous porous materials. DNN Deep neural network FE Finite element FEA Finite element analysis FEM Finite element method GAN Generative adversarial network GNN Graph neural network KNN K-nearest neighbor LSTM Long short-term memory ML Machine learning MLMM Machine learning material model NURBS Non-uniform rational B-spline ODE Ordinary dierential equation Nov 18, 2022 · In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations and optimizing the design to find the best Jan 23, 2024 · Currently, literature on deep learning-enabled finite element analysis for pipelines SCC prediction is scarce, offering limited insights into this emerging approach and lack of comprehensive review. The Finite Element Method-enhanced Neural Network hybrid model Oct 15, 2020 · The hierarchical neural networks and the deep learning have been applied to many applications of the computational mechanics or the finite element method, which can be classified into two categories: One is the simple combination of the conventional finite element method with the neural networks or the deep learning, and the other is such that a Deep Learning-based surrogate outperforms other regression models on such sparse data. However, most of these approaches require experimental parameters or rely on time-consuming nonlinear finite element analysis. ymssp. 3390/ma17030643 Corpus ID: 267428131; Accurate Finite Element Simulations of Dynamic Behaviour: Constitutive Models and Analysis with Deep Learning @article{Zhang2024AccurateFE, title={Accurate Finite Element Simulations of Dynamic Behaviour: Constitutive Models and Analysis with Deep Learning}, author={Yiwei Zhang and Chengcheng Guo and Yahui Huang and Ruizhi Zhang and Jian Zhang and Oct 31, 2023 · Finite element model updating is a technique for establishing a finite element model that can reflect the current state of a target structure based on the measured responses. May 16, 2023 · The hierarchical deep-learning neural network (HiDeNN) is systematically developed through the construction of structured deep neural networks (DNNs) in a hierarchical manner, and a special case of HiDeNN for representing Finite Element Method (or Jan 1, 2022 · The finite element (FE) is activated through an iterative procedure to improve the solution accuracy without mesh refinement. Guo et al. This study employs deep learning techniques to investigate the impact of different Styrene–Butadiene–Styrene (SBS) modifiers on asphalt microstructures, akin to bee structures. Apley , Gregory J. Aug 20, 2023 · Liang L, Liu M, Martin C, Sun W (2018) A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis. The work also focused on normalized data generation for efficient training of the model, preprocessing of the inputs to the network, and finally post-processing of the output obtained from the ANN model to get the Feb 1, 2023 · In this study, a novel framework for the finite element (FE) analysis of geotechnical engineering problems is proposed, in which a deep learning (DL) model is employed to depict the constitutive behaviors of soils, circumventing the difficulties associated with conventional approaches. [58] proposed a deep collocation method (DCM) based on transfer learning for solving potential problems in non-homogeneous media. FE simulations were performed using the three-layer LIB model, which was previously utilized by Xia et al. J R Soc Interface 15 (138):20170844. In LIBs, the process of lithium-ion Sep 17, 2021 · With ground truth (empirical evidence) data being generated from a finite-element analysis solver, a deep convolutional neural network is trained in a supervised manner to learn a mapping for Dec 5, 2022 · Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-time analysis. Wagner , Wing Kam Liu * We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. We calculated Dice coefficients to evaluate the similarity of the manual and automatic segmentation. Oishi and Yagawa [30] utilized deep learning to increase the accuracy of numerical integration when calculating the stiffness matrix of finite elements. 3390/ma17030643. The numerical scheme (2) is stable with respect to L. I. norm ·, namely there exists a positive constant C which is independent ofZ and T such that u. Given the massive amount of data generated daily, there is a need for even faster and more effective machine learning algorithms that can provide updated models for real-time applications and on-demand tools. Finite element analysis is employed to study the band gaps of samples. Two models are presented: the Point-Net classification model and the Dynamic Graph Convolutional Neural Net model, both trained point-cloud deep learning models performed well on experiments with finite element analysis arising from automotive industry. The main idea is to use a neural network to learn the solution map of the PDEs and to do so in an element-wise fashion. 1 depicts workflows of the multiscale mechanics modeling of a heterogeneous macro-structure using three different approaches: (a) full-scale FE analysis: the full-scale macro-structure with explicit representation of the microstructure is analyzed using an FE approach, (b) F E 2 analysis: a two-scale modeling scheme is implemented by performing micro-scale FE analyses to predict effective Jun 1, 2023 · However, small time computational steps and fine element mesh discretization are necessary for dynamic response analysis of dams through finite element method, so as to ensure the solution stability and accuracy. 202000031 Corpus ID: 219454107; Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials @article{Zhang2020FiniteElementBasedDM, title={Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials}, author={Zhizhou Zhang and Grace X. 1002/adts. The results Our proposed method uses deep neural networks in the form of convolutional neural networks (CNN) to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions. / Hiraide, Kazuya; Oya, Yutaka; Suzuki, Misato et al. 37, 107233, 12. May 1, 2023 · It is an extension of the deterministic finite element (FE) approach and is able to treat random effects by modeling uncertainties during the simulation of engineering problems [10]. Feb 2, 2019 · Liang L, Liu M, Martin C, Sun W (2018) A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis. Deep Learning and Finite Element Method for Physical Systems Modeling - oleksiyskononenko/mlfem Design of Efficient Finite Elements Using Deep Learning Approach Sekhor S. A mode-based finite element formulation is devised for a four-node finite element and the assumed modal strain is employed for bending modes. In this paper, a novel deep learning based structural finite element analysis surrogate model is developed to speed up the structural analysis process. Apr 15, 2021 · The concept of Finite Element Network Analysis (FENA) is first introduced. Recently, researchers have tried replacing numerical simulations with machine learning (ML) models to predict the output at a much higher speed. In this section, a classic three-layer LIB model is built, as described in Fig. DNNs can model complicated, nonlinear relation-ships between input and output data. Shipbuilding Menu Toggle. (8)). Liravi F (2014) Dynamic force analysis for bottom-up projection-based Additive Manufacturing using finite element analysis. May 17, 2020 · The design process for such smart materials requires iterations of finite-element simulations that are computationally expensive. Sep 1, 2023 · In this study, a deep learning framework for multiscale finite element analysis (FE 2) is proposed. A novel artificial neural DOI: 10. FEM model of PMSM is established Nov 1, 2022 · The accuracy of the FEM solution is known to be improved when dividing the analysis domain into smaller elements, while the computation time increases explosively. May 13, 2023 · We introduce a novel hybrid methodology that combines classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. 1007/s11831-024-10063-0 Corpus ID: 268159562; Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review @article{Nath2024ApplicationOM, title={Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review}, author={Dipjyoti Nath and Ankit and Debanga Raj Neog and Sachin Singh Gautam}, journal={Archives of Jan 28, 2024 · In this study, calculation accuracy was improved by analysing the impact of constitutive models using the finite element (FE) model, and the deep learning (DL) model was employed for result analysis. 2023. Marine and Shipbuilding Industry: Finite Element and CFD Based Simulation and Design – Our experience include: Fatigue assessment studies, Modal and vibration analyses, Seakeeping and seaworthiness assessment, Maneuvering studies, Simulation and evaluation of systems, Damage surveys and investigations, Tie-down structural calculations and approval, Collision While these building blocks can be applied to any element, specific implementations are presented in 1D and 2D to illustrate the application of the deep learning neural network. 3 Stability Analysis for Linearized Finite Element Schemes . Article Google Scholar Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. Apr 13, 2021 · Finite element analysis (FEA), finite volume methods (FVM) and finite different time domain (FDTD) have increased solver efficiency while dynamic visualization techniques improve what is often called user-friendliness. 6 The stress concentration factor (k t) is an indication of the severity of the stress concentration, and it is determined as the ratio of the maximum peak stress and the nominal stress. This map takes input of the element geometry and the PDEs' parameters on that element, and Mar 12, 2022 · After presenting the mechanical model, by using Finite Element Analysis (FEA), a deep learning neural network model is developed for small-scale structures and the capability of this model in predicting different mechanical behavior under thermo-mechanical loading is investigated. C u. Dec 1, 2020 · In this paper, we propose a method that employs deep learning, an artificial intelligence technique, to generate stiffness matrices of finite elements. , Feng, P. 45 Several joint types with varying levels of multiaxiality and stress Nov 1, 2022 · This study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications and uses deep neural networks in the form of convolutional neural networks to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions. Our method is applied to three benchmark examples: a cantilever beam, an L-shape and a liver model subject to moving punctual loads. The pressure vessel contains dry electronics, power sources, and other sensors that can not be flooded. Computational Mechanics, 67:207–230) provides a systematic approach to constructing numerical approximations that can be incorporated into a wide variety of Partial differential equations (PDE) and/or Ordinary differential equations (ODE) solvers. , Liu, S. Finite element analysis of residual stresses and thin plate distortion after face milling. 1. In order to model more realistic real-world systems, simulation models have become more complex, and calculation becomes more expensive as a result. In each case, the framework is validated by direct comparison against the solutions available from analytical methods or traditional finite element analysis. proposed a DL model, called the deep learned finite element. 50- 53, 10. Mar 1, 2019 · To do this, we first describe the geometric multiscale finite element method [11], and then we obtain an identical result using linear regression. The Finite Element Method (FEM) has the potential to bridge this data gap by developing simulations that encapsulate the underlying physical phenomena in the form of complex Nov 27, 2023 · Mathematical analysis of deep neural networks (DNNs) from a finite element perspective; Development of theories, algorithms, and applications for convolutional neural networks (CNNs) and Transformers, drawing inspiration from multigrid structures; Investigation into the learning of data with low-dimensional structures. 105120 Corpus ID: 253507039; Finite element geotechnical analysis incorporating deep learning-based soil model @article{Guan2023FiniteEG, title={Finite element geotechnical analysis incorporating deep learning-based soil model}, author={Qingqing Guan and Z. Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond Ye Lu * , Hengyang Li, Lei Zhang, Chanwook Park, Satyajit Mojumder, Stefan Knapik, Zhongsheng Sang, Shaoqiang Tang, Daniel W. Jan 9, 2024 · As an alternative, a deep learning model (DLM) is employed to replace the representative volume element (RVE) in the DEM. Oct 1, 2023 · DOI: 10. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations and optimizing the design to find the best deep learning, neural network, finite-element analysis, stress analysis Author for correspondence: Wei Sun e-mail: wei. A search procedure for optimal bending directions Jul 11, 2024 · 3. The objective is to classify automatically the results of Nov 1, 2022 · A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis J R Soc Interface , 15 ( 138 ) ( 2018 ) , Article 20170844 Crossref View in Scopus Google Scholar Finite Element Analysis (FEA) is a numerical method for studying mechanical behavior of fluids and solids. 1109/DCABES57229. 108632 Corpus ID: 271026809; Finite element data-driven deep learning-based tensile failure analysis of precast bridge slab joint @article{Zhao2024FiniteED, title={Finite element data-driven deep learning-based tensile failure analysis of precast bridge slab joint}, author={Weijian Zhao and Qiliang Zhao and Bochao Sun and Hitoshi Takeda and Tatsuya Usui and Deep learning (DL) with integration of FEA leverages large-scale data and learn complex nonlinear patterns for SCC prediction. Feb 1, 2023 · In this study, a novel framework for the finite element (FE) analysis of geotechnical engineering problems is proposed, in which a deep learning (DL) model is employed to depict the constitutive behaviors of soils, circumventing the difficulties associated with conventional approaches. In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. edu A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis Liang Liang, Minliang Liu, Caitlin Martin and Wei Sun Nov 18, 2022 · In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. Motivation Nowadays, the vast majority of analysis in structural mechanics, fluid dynamics, electromagnetics and many other areas is based onthe finite element method (FEM) for solving boundary value problems. This framework consists of two deep learning models—one aimed at generating phase separation structures, and the other at predicting properties from Aug 4, 2023 · In this paper, we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations (PDEs). Apr 11, 2023 · The hierarchical deep-learning neural network (HiDeNN) (Zhang et al. Oct 1, 2023 · Consequently, using historical data from finite element analysis and deep learning algorithms, you can create an excellent data-driven model demonstrating how inputs affect how a machine operates and predicting its future behavior under the same inputs [43], [44], [45], [46]. Numerical analysis methods, including structural finite-element analysis (FEA) and computational fluid dynamics (CFD), are used to perform stress analysis of complex structures and systems in which it may be difficult to obtain an analytical solution. By creating the surrogate we speed up the prediction on the other design much faster than direct Finite Aug 24, 2021 · This paper introduces a new concept called self-updated finite element (SUFE). W. Dec 6, 2019 · Deep-Learning for finite element analysis (DLFEA) Learning the deformation biomechanics of heart valves involves learning multiple physical phenomena by the DLFEA framework. Jul 15, 2024 · Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation Comput Methods Appl Mech Eng , 414 ( 2023 ) , Article 116131 View PDF View article View in Scopus Google Scholar Feb 10, 2021 · Ma, Y. A bilinear cohesive model is adopted to describe the bond stress–slip constitutive behavior between the steel reinforcement and the surrounding concrete. First, the lamb wave propagation processes in thin structures containing flaws were simulated using the SBFEM, and the echo wave signal at an observation point was recorded and May 16, 2023 · Hughes TJ (2012) The finite element method: linear static and dynamic finite element analysis. The employed deep learning model was trained on a diverse In this paper, a method of deep learning is built to reduce the needed time on performance analyze and optimization of permanent magnet synchronous motor (PMSM). This study investigates the utilization of Mar 1, 2024 · Request PDF | On Mar 1, 2024, Dipjyoti Nath and others published Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review | Find, read and cite all the Aug 29, 2022 · After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element Jul 5, 2024 · A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis J. 17 Convolutional Neural Net model respectively. propose a Deep Learning (DL) method [1,2] to construct deep neural networks (DNN), which, once trained, allow bypass of FEA. Nov 17, 2022 · In this paper, an element-based deep learning approach named DeepFEM for solving nonlinear partial differential equations (PDEs) in solid mechanics is developed to reduce the number of sampling points required for training the deep neural network. Models for Finite Element Analysis. Mar 1, 2024 · To compute the element stiffness matrix, Jung et al. MATLAB offers a perfect tool to visualize finite element analysis method. Jan 24, 2018 · 1. . Guo and Z. NA] 18 Nov 2022 November 21, 2022 Abstract In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. Sep 1, 2023 · Fig. Nov 1, 2022 · Download Citation | Improvement of Finite Element Solutions with Deep Learning | The accuracy of the FEM solution is known to be improved when dividing the analysis domain into smaller elements May 17, 2020 · DOI: 10. gatech. Feb 3, 2023 · Four datasets of various shapes of BGs (9000 2D images) generated by a finite element analysis showed that the deep neural network (DNN) model could efficiently predict the mechanical properties of the composite hydrogel, including the Young’s modulus and Poisson’s ratio. Make your own FEA solver with MATLAB™️ Using MATLAB App Designer™️ create a professional looking FEA solver without having to be a software developer. The analysis of the electromagnetic speed, torque and efficiency of PMSM is carried on with Finite Element Method (FEM), which is 8 pole-pairs, 48 stator slots and 195mm of stator external diameter. Mar 1, 2024 · DOI: 10. However, the substantial costs and time requirements associated with multi-scale FEM analyses have prompted a growing interest in adopting more efficient, machine-learning-driven strategies. Computational Mechanics, 67:207–230) provides a systematic approach to constructing numerical Compared to traditional mesh-based methods, such as the finite difference and the finite element methods, deep learning offers a mesh-free approach by taking advantage of automatic differentiation, which can overcome the limitation in dimension and in complexity of boundary shape, allowing for irregularity. Math Comput 68(228):1521–1531. 0844 Oct 1, 2022 · Request PDF | On Oct 1, 2022, Meduri Venkata Shivaditya and others published Point-Cloud-based Deep Learning Models for Finite Element Analysis | Find, read and cite all the research you need on Jun 5, 2023 · The finite element analysis is the simulation of any given physical phenomenon using a numerical technique called finite element method (FEM). 2302906 Corpus ID: 267220408; Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction Feb 1, 2023 · DOI: 10. This can prove extremely valuable in real-time structural assessment applications. fwxqnuo wpscvdyf unyoyiz kwujnupm xxc ezsela dwagp tcrsl iloq uqj