Finite element analysis deep learning. " Journal of The Royal Society Interface 15, no.
Finite element analysis deep learning In this study, a novel framework for the finite element (FE) analysis of geotechnical engineering problems is Jul 15, 2024 · In this study, an innovative approach based on artificial intelligence and multiscale finite element method is presented. Its reliability directly affects the service life of hydro-generator, thus further impacts the power generation, which is related to the national economy and people's livelihood. The proposed models show promise in automatizing the analysis process of finite element simulations. Instead, Deep Learning (DL) techniques can generate results significantly faster than Abstract Machine learning (ML) has evolved as a technology used in even broader domains, ranging from spam detection to space exploration, as a result of the boom in available data and afordable computing power in recent years. The results Keywords deep learning, finite element analysis, surrogate modeling, random forest, gradient boost, sub-sea pressure hull II. This coupled approach captures the interaction between temperature and mechanical deformations, as the temperature field influences the material’s mechanical response, and vice versa. Recent advancements in machine learning have led to surrogate models capable of accelerating FEA. However, these methods still have several limitations, mainly with respect to the dynamic prediction of accurate solutions independently from the original finite element model and/or respective ground truth data. Jul 28, 2023 · This research harnesses 2D cross-sectional imagery to establish a surrogate model for finite element analysis, offering an accurate and efficient approach for predicting stress fields in composite material design, irrespective of geometric complexity or boundary conditions. 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]. Motivated by this research gap, this study proposes 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. Recent advancements in machine learning have introduced surrogate models that can accelerate FEA. . Abstract Finite-element analysis (FEA) for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures. To find field variables in a domain under investigation, partial diferential equations (PDEs) are solved using the numerical method known as finite element method (FEM 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). Deep Learning and Finite Element Method for Physical Systems Modeling - oleksiyskononenko/mlfem Apr 11, 2023 · The hierarchical deep-learning neural network (HiDeNN) (Zhang et al. Nov 18, 2022 · In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. The objective is to classify automatically the results of the simulations without tedious human intervention. To find field variables in a domain under investigation, partial differential equations (PDEs) are solved using the numerical method known as finite element method Mar 1, 2025 · This paper proposes a deep finite element method (DFEM), which integrates physics-informed neural networks (PINNs) with the finite element method (FEM). 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. Both trained point-cloud deep learning models performed well on experiments with finite element analysis arising from automotive industry. Aug 20, 2023 · The finite element method (FEM) is a well-known method for numerically solving partial differential equations (PDEs) over a physical domain. Apr 15, 2025 · Finite Element Analysis (FEA) is a computationally intensive method for simulating physical phenomena. This paper presents a framework of the nonlinear finite element based on Sep 11, 2024 · "A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis. Aug 16, 2025 · Finite element analysis (FEA) of fused deposition modeling (FDM) has recently been recognized in additive manufacturing (AM) for predictions in temperature and displacement. Both trained point-cloud deep Aug 3, 2024 · In this comprehensive guide, we delve deep into the fascinating world of applying machine learning algorithms to finite element analysis, exploring the underlying principles, methodologies, and real-world applications. These predictions can be invaluable for making corrections to the printing process to improve quality of printed components. We begin with a brief introduction of the traditional FEA process Speaker: Panos Pantidis (New York University Abu Dhabi, United Arab Emirates) Title: Accelerating FEM with machine learning: an introduction to the Integrated Finite Element Neural Network (I-FENN). The development of various design systems continuously imposes higher demands on computational costs while preserving accuracy. The lower frame is one of the most important components of hydro-generator. Mar 1, 2024 · Abstract Machine learning (ML) has evolved as a technology used in even broader domains, ranging from spam detection to space exploration, as a result of the boom in available data and affordable computing power in recent years. Aug 18, 2021 · Here, this task is accomplished by combining machine learning (ML) and finite element analysis (FEA). Dec 1, 2024 · UHTCMCs is extremely complex, making it particularly challenging to conduct precise heat transfer analysis that accurately reflects the material's true structural features due to their heterogeneous multiphase characteristics. We select and discuss several losses employing preconditioners and different norms to enhance convergence 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 Oct 31, 2023 · Finite element analysis FE model description An FE model was constructed for the tested structure to analyze the characteristics of various damaged conditions for the structure. Yet there are still limitations in developing surrogates of transient FEA models that can simultaneously predict the solutions for both nodes and elements with applicability on both Sep 1, 2023 · In this study, a deep learning framework for multiscale finite element analysis (FE2) is proposed. INTRODUCTION Design problems in the engineering and scientific com-munity are an ongoing challenge and in most cases, these design problems include the complex relationship between design parameters. This approach involves partitioning the entire composite material structure into coarse grids that resemble homogenous structures of similar size, providing results consistent to fine-grid finite element analysis. The connections between neurons in the architecture mimic the Finite Element connectivity graph when applying mesh refinements. FEINN In this study, we propose a novel deep learning model named as the Finite-element-informed neural network (FEI-NN), inspired from finite element method (FEM) for parametric simulation of static problems in structural mechanics. The dataset contains 365 points comprising four adhesives with four different joint types. Macroscopic strain and stress data are directly assigned to Dec 5, 2022 · Finite-element analysis (FEA) for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures. " Journal of The Royal Society Interface 15, no. 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 FE2 analysis. The result is an approximated model of the element that can be used in the same context. Both trained point-cloud deep Apr 15, 2025 · Finite Element Analysis (FEA) is a computationally intensive method for simulating physical phenomena. 138 (2018): 20170844. Conventional methods, such as FEA, provide high fidelity results but require the solution of large linear systems that can be computationally intensive. However, patient-specific FEA models usually require complex Jun 6, 2022 · In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simulation process. The residual from finite element methods and custom loss functions from neural networks are merged to form the algorithm. However, there are limitations in developing surrogates of transient FEA models that can handle dynamic input and multi-dimensional output. In this study, we propose a heat transfer model that combines deep learning with finite element analysis, which is used for feature extraction and heat transfer analysis 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. For example, the fine May 3, 2022 · obscure formu-lations and suffer from poor applicability in engineering practice. By creating the surrogate we speed up the prediction on the other design much faster than direct Finite element Analysis. It has been applied successfully to solve various problems in the field of structural analysis, electromagnetics, heat 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. Introduction Stress analysis is an essential part of engineering and design. Dec 10, 2015 · 1. Two models are here presented: the Point-Net classification model and the Dynamic Graph Convolutional Neural Net model. This paper reviews and investigates the current research directions and applications of DL-enabled FEA methodologies for simulation of SCC prediction. The meth… A three-dimensional finite element (FE) simulation of the welding process is performed using coupled thermo-elasto-plastic analysis in ABAQUS 2021. Dec 5, 2024 · Finite Element Analysis (FEA) is a powerful but computationally intensive method for simulating physical phenomena. Apr 22, 2025 · In conclusion, the application of deep learning-based segregation combined with finite element analysis in paleontology offers researcher a precise and efficient approach to study the biomechanics Apr 15, 2021 · This paper introduces the concept of finite element network analysis (FENA) which is a physics-informed, machine-learning-based, computational framework for the simulation of physical systems. Conventional methods, such as FEA, provide high fidelity solutions but require solving large linear systems that can be computationally intensive. 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. Oishi and Yagawa [30] utilized deep learning to increase the accuracy of numerical integration when calculating the stiffness matrix of finite elements. In this paper, we explore point-cloud based deep learning models to analyze numerical simulations arising from finite element analysis. Currently, finite element analysis (FEA) is the most used method to perform structural reliability analysis. Motivated by this research gap, this study proposes 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. Numerical analysis methods, such as structural finite element analysis (FEA), are typically used to conduct stress analysis of various Jun 24, 2024 · Towards finite element simulation using deep learning 2 minute read Paper Motivation Finite-element (FE) modeling is the standard approach for representing soft-tissue structures in biomechanical simulations. 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. FE models, however, incur a substantial computational cost due to having thousands of degrees-of-freedom. However, due to the huge size Mar 1, 2025 · This paper proposes a deep finite element method (DFEM), which integrates physics-informed neural networks (PINNs) with the finite element method (FEM). The DFEM provides a versatile and robust framework that seamlessly combines observational data with physical laws. Jan 15, 2025 · This paper presents a machine learning-based finite element construction method (MLBFE) to predict a precise strain field with minimal nodes. However, FEA has limitations that discourage manufacturers from using it. Instead, deep learning (DL) techniques can generate solutions significantly faster than Mar 1, 2019 · To generate our model, we choose an existing finite element formulation, extract data from an instance of that element, and feed that data to the machine learning algorithm. 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. This study proposes a deep learning-based framework for FEA (DeepFEA) that copes with these limitations. o5a8icfn8th3e4l0wtu6agsavtddexq830bkujge5m