A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2022, Номер 401, С. 115616 - 115616

Опубликована: Сен. 20, 2022

Язык: Английский

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Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance

Journal of the Mechanics and Physics of Solids, Год журнала: 2022, Номер 172, С. 105177 - 105177

Опубликована: Дек. 15, 2022

Язык: Английский

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Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 415, С. 116277 - 116277

Опубликована: Июль 28, 2023

Язык: Английский

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Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Journal of Computing and Information Science in Engineering, Год журнала: 2024, Номер 24(4)

Опубликована: Янв. 8, 2024

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep push scientific forward a range of disciplines, such as fluid mechanics, solid materials science, etc. The incorporation neural networks is particularly crucial this hybridization process. Due their intrinsic architecture, conventional cannot be successfully trained scoped when data are sparse, which the case many engineering domains. Nonetheless, provide foundation respect physics-driven or knowledge-based constraints during training. Generally speaking, there three distinct network frameworks enforce underlying physics: (i) physics-guided (PgNNs), (ii) physics-informed (PiNNs), (iii) physics-encoded (PeNNs). These methods advantages for accelerating numerical modeling complex multiscale multiphysics phenomena. In addition, recent developments operators (NOs) add another dimension these new simulation paradigms, especially real-time prediction systems required. All models also come with own unique drawbacks limitations that call further fundamental research. This study aims present review four (i.e., PgNNs, PiNNs, PeNNs, NOs) used state-of-the-art architectures applications reviewed, discussed, future research opportunities presented terms improving algorithms, considering causalities, expanding applications, coupling solvers.

Язык: Английский

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A deep learning energy-based method for classical elastoplasticity

International Journal of Plasticity, Год журнала: 2023, Номер 162, С. 103531 - 103531

Опубликована: Янв. 20, 2023

Язык: Английский

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Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

Thin-Walled Structures, Год журнала: 2024, Номер 205, С. 112495 - 112495

Опубликована: Сен. 24, 2024

Язык: Английский

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Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(5), С. 2945 - 2984

Опубликована: Март 1, 2024

Язык: Английский

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A Review on Data-Driven Constitutive Laws for Solids

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 3, 2024

Язык: Английский

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Methods for enabling real-time analysis in digital twins: A literature review

Computers & Structures, Год журнала: 2024, Номер 297, С. 107342 - 107342

Опубликована: Апрель 4, 2024

This paper presents a literature review on methods for enabling real-time analysis in digital twins, which are virtual models of physical systems. The advantages twins numerous, including cost reduction, risk mitigation, efficiency enhancement, and decision-making support. However, their implementation faces challenges such as the need data analysis, resource limitations, uncertainty. focuses reducing computational demands, have not been systematically discussed literature. reviews categorizes tools accelerating modeling phenomena needs twins.

Язык: Английский

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A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems

Multiscale Science and Engineering, Год журнала: 2024, Номер 6(1), С. 1 - 11

Опубликована: Фев. 13, 2024

Язык: Английский

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