Singh, V.; Harursampath, D.; Dhawan, S.; Sahni, M.; Saxena, S.; Mallick, R. Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem. Modelling2024, 5, 1532-1549.
Singh, V.; Harursampath, D.; Dhawan, S.; Sahni, M.; Saxena, S.; Mallick, R. Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem. Modelling 2024, 5, 1532-1549.
Singh, V.; Harursampath, D.; Dhawan, S.; Sahni, M.; Saxena, S.; Mallick, R. Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem. Modelling2024, 5, 1532-1549.
Singh, V.; Harursampath, D.; Dhawan, S.; Sahni, M.; Saxena, S.; Mallick, R. Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem. Modelling 2024, 5, 1532-1549.
Abstract
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 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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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