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Physics-informed neural network solution of 2nd order ODE:s

version 1.0.0 (4.48 KB) by Andreas Almqvist
A PINN employed to solve c(x)y''+c'(x)y'-f = 0, y(0)=y(1)=0, using symbolic differentiation and the gradient decent method.

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Updated 30 Jul 2021

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This rutine presents the design of a physics-informed neural networks applicable to solve initial- and boundary value problems described by linear ODE:s. The objective not to develop a numerical solution procedure which is more accurate and efficient than standard finite element or finite difference based methods, but to present the concept of the construction of a PINN, in the context of hydrodynamic lubrication. It is, however, worth to notice that the present PINN, contrary to FEM and FDM, is a meshless method and that it is not a datadriven machine learning program. This concept may, of course, be generalised, and perhaps it turns out to be more accurate and efficient than existing routines in solving related but nonlinear problems. This is, however, scope of future research in this direction.

Cite As

Andreas Almqvist (2021). Physics-informed neural network solution of 2nd order ODE:s (https://www.mathworks.com/matlabcentral/fileexchange/96852-physics-informed-neural-network-solution-of-2nd-order-ode-s), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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