Analysis of the physics-informed neural network approach to solving ordinary differential equations

Konyukhov I.V., Konyukhov V.M., Chernitsa A.A., Dyussenova A.
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Considered the application of physics-informed neural networks using multi layer perceptrons to solve Cauchy initial value problems in which the right-hand sides of the equation are continuous monotonically increasing, decreasing or oscillating functions. With the use of the computational experiments the influence of the construction of the approximate neural network solution, neural network structure, optimization algorithm and software implementation means on the learning process and the accuracy of the obtained solution is studied. The analysis of the efficiency of the most frequently used machine learning frameworks in software development with the programming languages Python and C# is carried out. It is shown that the use of C# language allows to reduce the time of neural networks training by 20–40%. The choice of different activation functions affects the learning process and the accuracy of the approximate solution. The most effective functions in the considered problems are sigmoid and hyperbolic tangent. The minimum of the loss function is achieved at the certain number of neurons of the hidden layer of a single-layer neural network for a fixed training time of the neural network model. It’s also mentioned that the complication of the network structure increasing the number of neurons does not improve the training results. At the same time, the size of the grid step between the points of the training sample, providing a minimum of the loss function, is almost the same for the considered Cauchy problems. Training single-layer neural networks, the Adam method and its modifications are the most effective to solve the optimization problems. Additionally, the application of twoand three-layer neural networks is considered. It is shown that in these cases it is reasonable to use the LBFGS algorithm, which, in comparison with the Adam method, in some cases requires much shorter training time achieving the same solution accuracy. The specificity of neural network training for Cauchy problems in which the solution is an oscillating function with monotonically decreasing amplitude is also investigated. For these problems, it is necessary to construct a neural network solution with variable weight coefficient rather than with constant one, which improves the solution in the grid cells located near by the end point of the solution interval.

Keywords: ordinary differential equations, machine learning, physics-informed neural networks, numerical methods
Citation in English: Konyukhov I.V., Konyukhov V.M., Chernitsa A.A., Dyussenova A. Analysis of the physics-informed neural network approach to solving ordinary differential equations // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1621-1636
Citation in English: Konyukhov I.V., Konyukhov V.M., Chernitsa A.A., Dyussenova A. Analysis of the physics-informed neural network approach to solving ordinary differential equations // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1621-1636
DOI: 10.20537/2076-7633-2024-16-7-1621-1636
URL: http://crm-en.ics.org.ru/journal/article/3558/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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