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Applying Physics-Informed Extreme Learning Machines to Solve the Euler-Bernoulli Beam Problem
- 1 Shanghai World Foreign Academy
- 2 Imperial College London
* Author to whom correspondence should be addressed.
Abstract
Physics-Informed Neural Networks (PINNs) have been recently utilised to solve forward and backward partial differential equation problems. In this paper, we explore an alternative approach by using Physics-Informed Extreme Learning Machines (PIELM) to address the Euler-Bernoulli beam problem. We experiment with different types of functions combined with various numbers of hidden layers to identify the most effective techniques and conditions for solving partial differential equations with greater efficiency and accuracy. Both the traditional PINN and PIELM methods are applied, and their results are compared. Our findings demonstrate that PIELM offers more efficient computation while maintaining comparable accuracy in the results.
Keywords
Beam theory, partial differential equations, physics-informed neural networks, extreme learning machines
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Cite this article
Pan,S.;Li,H. (2024). Applying Physics-Informed Extreme Learning Machines to Solve the Euler-Bernoulli Beam Problem. Theoretical and Natural Science,53,133-142.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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