DeepOKAN: Deep operator network based on Kolmogorov Arnold networks for mechanics problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2025, Номер 436, С. 117699 - 117699

Опубликована: Янв. 14, 2025

Язык: Английский

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Data-driven methods for flow and transport in porous media: A review

International Journal of Heat and Mass Transfer, Год журнала: 2024, Номер 235, С. 126149 - 126149

Опубликована: Сен. 7, 2024

Язык: Английский

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State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Artificial Intelligence Review, Год журнала: 2024, Номер 57(8)

Опубликована: Июль 5, 2024

Abstract Significant uncertainties can be found in the modelling of geotechnical materials. This attributed to complex behaviour soils and rocks amidst construction processes. Over past decades, field has increasingly embraced application artificial intelligence methodologies, thus recognising their suitability forecasting non-linear relationships intrinsic review offers a critical evaluation AI methodologies incorporated computational mechanics for engineering. The analysis categorises four pivotal areas: physical properties, mechanical constitutive models, other characteristics relevant Among various analysed, ANNs stand out as most commonly used strategy, while methods such SVMs, LSTMs, CNNs also see significant level application. widely algorithms are Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), representing 35%, 19%, 17% respectively. extensive is domain accounting 59%, followed by applications at 16%. efficacy intrinsically linked type datasets employed, selected model input. study outlines future research directions emphasising need integrate physically guided adaptive learning mechanisms enhance reliability adaptability addressing multi-scale multi-physics coupled problems geotechnics.

Язык: Английский

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I-FENN with Temporal Convolutional Networks: Expediting the load-history analysis of non-local gradient damage propagation

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 425, С. 116940 - 116940

Опубликована: Апрель 3, 2024

Язык: Английский

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A Finite Operator Learning Technique for Mapping the Elastic Properties of Microstructures to Their Mechanical Deformations

International Journal for Numerical Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

ABSTRACT To obtain fast solutions for governing physical equations in solid mechanics, we introduce a method that integrates the core ideas of finite element with physics‐informed neural networks and concept operators. We propose directly utilizing available discretized weak form packages to construct loss functions algebraically, thereby demonstrating ability find even presence sharp discontinuities. Our focus is on micromechanics as an example, where knowledge deformation stress fields given heterogeneous microstructure crucial further design applications. The primary parameter under investigation Young's modulus distribution within system. investigations reveal physics‐based training yields higher accuracy compared purely data‐driven approaches unseen microstructures. Additionally, offer two methods improve process obtaining high‐resolution solutions, avoiding need use basic interpolation techniques. first one based autoencoder approach enhance efficiency calculation high resolution grid points. Next, Fourier‐based parametrization utilized address complex 2D 3D problems micromechanics. latter idea aims represent microstructures efficiently using Fourier coefficients. proposed draws from deep energy but generalizes enhances them by learning parametric without relying external data. Compared other operator frameworks, it leverages domain decomposition several ways: (1) uses shape derivatives instead automatic differentiation; (2) automatically includes node connectivity, making solver flexible approximating jumps solution fields; (3) can handle arbitrary shapes enforce boundary conditions. provided some initial comparisons well‐known algorithms, emphasize advantages newly method.

Язык: Английский

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Accelerating composite sandwich plate analysis: A hybrid higher-order XFEM and ANN approach for natural frequency prediction

Mechanics Based Design of Structures and Machines, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Янв. 24, 2025

Язык: Английский

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A universal surrogate modeling method based on heterogeneous graph neural network for nonlinear analysis

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2025, Номер 437, С. 117793 - 117793

Опубликована: Фев. 1, 2025

Язык: Английский

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Implementation of isotropic hyperelastic material models: a »template« approach

Acta Mechanica, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Язык: Английский

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An efficient Poisson solver and a data-driven surrogate model for magnetic stray field calculations

Computational Mechanics, Год журнала: 2025, Номер unknown

Опубликована: Март 7, 2025

Язык: Английский

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StructureGraph: a universal performance evaluation method for engineering structures via heterogeneous graph neural network

Structural and Multidisciplinary Optimization, Год журнала: 2025, Номер 68(3)

Опубликована: Март 1, 2025

Язык: Английский

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