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- W3202725229 abstract "Several neural network approaches for solving differential equations employ trial solutions with a feedforward neural network. There are different means to incorporate the trial solution in the construction, for instance one may include them directly in the cost function. Used within the corresponding neural network, the trial solutions define the so-called neural form. Such neural forms represent general, flexible tools by which one may solve various differential equations. In this article we consider time-dependent initial value problems, which requires to set up the trial solution framework adequately.The neural forms presented up to now in the literature for such a setting can be considered as first order polynomials. In this work we propose to extend the polynomial order of the neural forms. The novel construction includes several feedforward neural networks, one for each order. The feedforward neural networks are optimised using a stochastic gradient descent method (ADAM). As a baseline model problem we consider a simple yet stiff ordinary differential equation. In experiments we illuminate some interesting properties of the proposed approach." @default.
- W3202725229 created "2021-10-11" @default.
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- W3202725229 date "2021-01-01" @default.
- W3202725229 modified "2023-10-02" @default.
- W3202725229 title "Polynomial Neural Forms Using Feedforward Neural Networks for Solving Differential Equations" @default.
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- W3202725229 doi "https://doi.org/10.1007/978-3-030-87986-0_21" @default.
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