Eighty Years of the Finite Element Method: Birth, Evolution, and Future
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Abstract (arXiv)
This year marks the eightieth anniversary of the invention of the finite element method (FEM). FEM has become the computational workhorse for engineering design analysis and scientific modeling of a wide range of physical processes, including material and structural mechanics, fluid flow and heat conduction, various biological processes for medical diagnosis and surgery planning, electromagnetics and semi-conductor circuit and chip design and analysis, additive manufacturing, i.e. virtually every conceivable problem that can be described by partial differential equations (PDEs). FEM has fundamentally revolutionized the way we do scientific modeling and engineering design, ranging from automobiles, aircraft, marine structures, bridges, highways, and high-rise buildings. Associated with the development of finite element methods has been the concurrent development of an engineering science discipline called computational mechanics, or computational science and engineering. In this paper, we present a historical perspective on the developments of finite element methods mainly focusing on its applications and related developments in solid and structural mechanics, with limited discussions to other fields in which it has made significant impact, such as fluid mechanics, heat transfer, and fluid-structure interaction. To have a complete storyline, we divide the development of the finite element method into four time periods: I. (1941-1965) Early years of FEM; II. (1966-1991) Golden age of FEM; III. (1992-2017) Large scale, industrial applications of FEM and development of material modeling, and IV (2018-) the state-of-the-art FEM technology for the current and future eras of FEM research. Note that this paper may not strictly follow the chronological order of FEM developments, because often time these developments were interwoven across different time periods.
Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jul 2021.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 7; 2025: 5; 2026: 4.
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- 2024On conditional diffusion models for PDE simulationscited by 60
- 2023Grounding Graph Network Simulators using Physical Sensor Observationscited by 15
- 2022Towards out of distribution generalization for problems in mechanicscited by 11
- 2024A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformationscited by 10
- 2025Automotive Crash Dynamics Modeling Accelerated with Machine Learningcited by 10
- 2023Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equationscited by 7
- 2023Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanicscited by 4
- 2024Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equationscited by 4
- 2025AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Predictioncited by 4
- 2024A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problemscited by 3
- 2024Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEscited by 3
- 2025Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problemscited by 2
9 more
- 2025Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decompositioncited by 2
- 2022MRF-PINN: A Multi-Receptive-Field convolutional physics-informed neural network for solving partial differential equationscited by 1
- 2025Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equationscited by 1
- 2024Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterialscited by 0
- 2024A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimensioncited by 0
- 2026Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decompositioncited by 0
- 2026VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysiscited by 0
- 2026Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometriescited by 0
- 2026One-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fieldscited by 0
- 2026One-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fields22 Jun 2026
- 2026Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries6 Jun 2026
- 2026VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis27 May 2026
- 2026Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition24 May 2026
- 2025Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition28 Nov 2025
- 2025Automotive Crash Dynamics Modeling Accelerated with Machine Learning17 Oct 2025
- 2025Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems24 Sep 2025
- 2025AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction29 May 2025
- 2025Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations22 Jan 2025
- 2024Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs7 Dec 2024
- 2024On conditional diffusion models for PDE simulations21 Oct 2024
- 2024A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension10 Oct 2024
9 more
- 2024A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problems3 Jun 2024
- 2024Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials19 Apr 2024
- 2024A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations28 Mar 2024
- 2024Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations4 Jan 2024
- 2023Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics21 Nov 2023
- 2023Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations9 Apr 2023
- 2023Grounding Graph Network Simulators using Physical Sensor Observations23 Feb 2023
- 2022MRF-PINN: A Multi-Receptive-Field convolutional physics-informed neural network for solving partial differential equations6 Sep 2022
- 2022Towards out of distribution generalization for problems in mechanics29 Jun 2022
- 2023Grounding Graph Network Simulators using Physical Sensor Observationscited by 15
- 2022Towards out of distribution generalization for problems in mechanicscited by 11
- 2024A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformationscited by 10
- 2025Automotive Crash Dynamics Modeling Accelerated with Machine Learningcited by 10
- 2023Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equationscited by 7
- 2023Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanicscited by 4
- 2024Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equationscited by 4
- 2025AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Predictioncited by 4
- 2024A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problemscited by 3
- 2024Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEscited by 3
- 2025Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problemscited by 2
- 2025Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decompositioncited by 2
12 most cited of 18 All 18 (JSON)
- 2026Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries6 Jun 2026
- 2026Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition24 May 2026
- 2025Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition28 Nov 2025
- 2025Automotive Crash Dynamics Modeling Accelerated with Machine Learning17 Oct 2025
- 2025Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems24 Sep 2025
- 2025AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction29 May 2025
- 2025Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations22 Jan 2025
- 2024Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs7 Dec 2024
- 2024A Variational Bayesian Inference Theory of Elasticity and Its Mixed Probabilistic Finite Element Method for Inverse Deformation Solutions in Any Dimension10 Oct 2024
- 2024A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problems3 Jun 2024
- 2024Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials19 Apr 2024
- 2024A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations28 Mar 2024
12 newest of 18 All 18 (JSON)