Ensemble of physics-informed neural networks for solving plane elasticity problems with examples

Mouratidou, Aliki D.; Drosopoulos, Georgios A.; Stavroulakis, Georgios E.

doi:10.1007/s00707-024-04053-3

Acta Mech

2024

DOI: 10.1007/s00707-024-04053-3

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Ensemble of physics-informed neural networks for solving plane elasticity problems with examples

Aliki D. Mouratidou,

Georgios A. Drosopoulos,

Georgios E. Stavroulakis

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

Singh,

Harursampath,

Dhawan

et al. 2024

Modelling

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

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

Singh,

Harursampath,

Dhawan

et al. 2024

Modelling

View full text Add to dashboard Cite

show abstract

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Ensemble of physics-informed neural networks for solving plane elasticity problems with examples

Cited by 1 publication

References 27 publications

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

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

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