Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem

Modelling—International Open Access Journal of Modelling in Engineering Science, Год журнала: 2024, Номер 5(4), С. 1532 - 1549

Опубликована: Окт. 18, 2024

Our objective in this work is to demonstrate how physics-informed neural networks, a type of deep learning technology, can be utilized examine the mechanical properties helicopter blade. The blade regarded as one-dimensional prismatic cantilever beam that exposed triangular loading, and comprehending its behavior utmost importance aerospace field. PINNs utilize physical information, including differential equations boundary conditions, within loss function network approximate solution. approach determines overall by aggregating losses from equation, data. We employed (PINN) an artificial (ANN) with equivalent hyperparameters solve fourth-order equation. By comparing performance PINN model against analytical solution equation results obtained ANN model, we have conclusively shown exhibits superior accuracy, robustness, computational efficiency when addressing high-order govern physics-based problems. In conclusion, study demonstrates offers alternative for solid mechanics problems applications industry.

Язык: Английский

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Outlier-resistant physics-informed neural network

Physical review. E, Год журнала: 2025, Номер 111(2)

Опубликована: Фев. 20, 2025

Recent advances in machine learning have introduced physics-informed neural networks (PINN) as a valuable tool for addressing dynamics through governing equations and experimental observations. Outliers can be present measurements significantly affect the accuracy of solutions provided by PINN. To overcome this limitation, we construct an outlier-resistant PINN (OrPINN) based on Tsallis statistics. We investigate robustness OrPINN describing acoustic linear elastic wave under various outlier-level scenarios. find that improve even when data is highly corrupted.

Язык: Английский

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Hierarchical Design of Mechanical Metamaterials: an Application on Pentamode-like Structures

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110232 - 110232

Опубликована: Апрель 1, 2025

Язык: Английский

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Physics-Informed Neural Networks for Predicting Internal Forces and Deformations of Structural Frames in a Single-Span Agricultural Greenhouse

Horticultural Science and Technology, Год журнала: 2025, Номер 43, С. 1 - 19

Опубликована: Апрель 24, 2025

Язык: Английский

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Multiscale Finite Elements Using Neural Network Material Metamodels

Advanced structured materials, Год журнала: 2025, Номер unknown, С. 363 - 375

Опубликована: Янв. 1, 2025

Язык: Английский

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